Bachelor of Science in Computer Science

This is an introductory calculus-based course covering Electricity, Magnetism and elements of Optics. The students will learn to identify fundamental laws in everyday electromagnetic phenomena and to apply these laws to solving basic physics problems and to describing laboratory experiments.

Course learning outcomes
1. Apply physics concepts and methods, including from other classes, using critical thinking.
2. Use acquired knowledge and gained skills to understand, set-up and solve qualitatively physics problems.
3. Employ the appropriate mathematical techniques and concepts to obtain quantitative solutions to problems.
4. Improve his/her communicating skills related to this course via reading the textbook and additional materials, doing homework problems, writing laboratory reports and doing optional in-class presentation.
5. Collect and analyze data and/or information from laboratory experiments and external sources.
6. Prepare coherent reports based on the accepted standards presented in class.
7. Meet the deadlines developing effective learning habits and discipline necessary to promote life-long learning.

This course covers foundations of probability theory, combinatorial and counting methods, conditional probability, random variables, discrete and continuous distributions, expectation, moment generating functions, multivariate distributions, variable transformations, the Law of Large Numbers and the Central Limit Theorem.

Prerequisite:MATH 162 Calculus II.

Course learning outcomes
1. Use basic counting techniques (multiplication rule, combinations, and permutations) to compute conditional probabilities directly and using Bayes' theorem and check for independence of events.
2. Introduce random variables and work with discrete and continuous random variables. In particular, understand the basic properties of Bernoulli, binomial, geometric Poisson distributions, uniform, Gamma and exponential, normal distribution and compute the central limit theorem.
3. Be able to compute the expectation, variance, covariance and correlation between jointly distributed variables, Moment generating functions, Transformation of random variables and the computation of order statistics.
4. Apply knowledge of probability to solve problems from the field of electronic, electrical and communications of applicable nature, falling in both discrete and continuous domain.

This course is a one-semester course intended for students majoring in Mathematics or Computer Science. It introduces the students to the fundamental concepts of mathematical reasoning. The main themes of the course are logic and proof, induction and recursion, discrete structures, set theory, combinatorics, algorithms, graph theory, and their applications. The students will learn how to formulate precise mathematical statements. The course also explores several common proof techniques and exposes the students to learning how to write mathematical proofs.

Course learning outcomes
1. Describe and analyze logical expressions.
2. Understand the elementary set theory.
3. Produce elementary mathematical proofs using different methods of proof.
4. Solve simple problems related to number theory, combinatorics, and graph theory.

This one-semester course is intended for mathematics, engineering and science students. It introduces students to the fundamental concepts of linear algebra. The course primarily deals with two mathematical objects: matrix and vector, and covers topics such as solutions of systems of linear equations, properties of invertible matrices, linear transformation, determinant, eigenvalues and eigenvectors, vector spaces and subspaces, linear independence, basis, coordinates, inner product, norm, orthogonal basis, similarity, and quadratic forms.

Course learning outcomes
1. Employ row reduction algorithm to solve systems of linear equations.
2. Understand basic matrix algebra and construct linear problems in the matrix forms.
3. Implement the basic theory of finite dimensional vector spaces and related concepts such as subspaces, basis, and linear transformations; Gram-Schmidt process.
4. Solve eigenvalue problems and apply matrix diagonalization procedures.

This course covers transcendental functions, advanced integration techniques, indeterminate forms, improper integrals, area and arc length in polar coordinates, infinite series, power series and Taylor’s theorem.

Course learning outcomes
1. Integrate functions whose antiderivative is given by elementary functions.
2. Use integrals in a variety of area and volume computations.
3. Solve first order differential equations by separation of variables or the method of integrating factors.
4. Analyze the convergence of series, which are either absolutely convergent or alternating.
5. Know the most usual Maclaurin series expansions and their intervals of convergence, together with how to derive them.
6. Work with curves in parametric form, especially polar coordinates.
7. Identify types of conic from their equation in Cartesian or polar coordinates.

Students will learn about common abstract data types such as stacks, queues, and maps, study how they can be utilized to solve different problems and implement them using the C++ language. The pros and cons of using different internal representations such as arrays, linked-lists, binary search trees, and hash tables will be examined. Students will be required to assess problems and choose appropriate data structures and relevant algorithms to improve performance and understand the practical limits of computability.

Course learning outcomes
1. Articulate the concept of abstract data types, and how they can be implemented in C++ using arrays and dynamically linked data structures.
2. Apply the most important abstract data types used in the storage and retrieval of information in modern software systems, including stack, queue, set, and map.
3. Analyze and quantify the relative performance of the data structures and underlying algorithms, and the scalability of the structures as the size of data sets increases;
4. Develop and test the data structures using component-based software engineering techniques;
5. Summarize and apply basic concepts of component-based design (e.g., encapsulation, data hiding, invariants) through their use of the C++ language.
6. Write proper C++ code that can be read, understood, and modified by others.

The course will address the design and fundamental organization of computing systems, from the representation of instructions and data to the flow of control and interaction with system components. Topics include memory management, storage systems, processor architectures, and low-level programming.

Course learning outcomes
1. Summarize how a computer is organized and designed.
2. Analyze computer systems (or subsystems) and compare them based on performance and cost.
3. Explain how high-level languages are executed on computer hardware (and architecture).
4. Program in assembly language using the MIPS/ARM instruction set architecture.
5. Summarize the concept of a memory hierarchy and caching.
6. Incorporate and integrate all the above into designing and evaluating a system.

The course provides an overview of the major conceptual paradigms of programming languages, from their theoretical foundations to practical implementation. Topics will include the programming language categories of imperative, logical, functional, and object-oriented. Students should possess mastery of at least one high-level language, and are expected to implement coding assignments in each of the paradigms as part of the course.

Course learning outcomes
1. Develop computer programs in different languages to solve problems and perform specified tasks, using proper programming practices and style.
2. Summarize and apply concepts related to object-oriented programming, such as generics, inheritance and polymorphism.
3. Compare and contrast the strengths and weaknesses of different programming languages.
4. Critically determine how to select an appropriate language for a given task.

In this course, students will be introduced to formal languages and mathematical models of computation. The topics of finite state and pushdown automata, regular expressions, and context-free grammars will be covered, along with applications such as text processing and compilers. The later portion of the course will cover Turing machines and the Church-Turing Thesis.

Course learning outcomes
1. Formulate regular expressions, context-free grammars, finite-state automata, and pushdown automata to express a given formal language.
2. Show the expressive equivalence of different language formalisms.
3. Classify a language within the Chomsky Hierarchy using techniques such as the Pumping Lemma.
4. Create a Turing Machine description to accept a given language.
5. Explain important concepts such as the Church-Turing Thesis, and why the Halting Problem cannot be solved computationally.

The course covers common strategies for designing efficient algorithms, and the analysis of their perfprmance in terms of time and memory. Topics such as searching and sorting, Greedy algorithms, divide-and-conquer techniques, dynamic programming, recurrence relations, and graph-based algorithms will be covered. A rigorous approach of asymptotic and probabilistic analysis of algorithms will be used in the course. Computational intractability and NP-completeness will also be discussed in this course.

Course learning outcomes
1. Use big-O, big-Ω and big-Θ notations to specify asymptotic bounds on time and space complexity of algorithms.
2. Use recurrence relations to determine the time complexity of recursively defined algorithms.
3. Implement and discuss the complexity of common and the most efficient sorting, searching, and hashing algorithms.
4. Solve problems using fundamental and intermediate graph algorithms with most efficient way.
5. Explain and analyze string-processing algorithms.
6. Utilize design techniques of algorithms, such as Divide and Conquer, Greedy and Dynamic Programming.
7. Explain the significance of NP-completeness and NP-Hardness, and give examples of NP-complete problems.

This course introduces the fundamentals of digital systems and microcontroller-based embedded systems, with emphasis on the integration of hardware and software in engineering applications. Students develop a solid foundation in digital logic design, including Boolean algebra, combinational and sequential circuits, and basic computer architecture concepts such as datapath and memory organization.
Building on these foundations, the course introduces microcontroller systems, focusing on instruction-level programming, peripheral interfacing, and system-level design. Students learn how computational models are implemented in physical hardware and how embedded systems interact with real-world signals.
The course includes extensive laboratory work using simulation tools and hardware platforms (e.g., FPGA boards and microcontrollers), where students design, implement, and debug digital and embedded systems from logic circuits to complete functional applications.

Course learning outcomes
1. Design and Analyze combinational logic circuits.
2. Understand the fundamentals of digital circuit elements such as registers, multiplexers, decoders, encoders etc.
3. Implement/Design and optimize synchronous/asynchronous sequential circuits.
4. Understand the how the central processing unit (CPU) in a computer works.
5. Demonstrate basic skills in practical aspects of digital circuits and systems, and their use in more complex systems.
6. Demonstrate understanding of the various hardware realizations of the basic digital circuit elements.
7. Demonstrate basic skills in using computer tools to design simple microcontroller systems.

This course will introduce the baseic principles in artificial intellegence. Students will learn simple representation schemes, problem solving paradigms, constraint propagation, and search strategies. Students will study areas of AI application such as knowledge representation, natural language processing, expert systems, vision and robotics. Upon completionm students should be able to develop intelligent systems, understand the role of knowledge representation, problem solving, learning in intellegent-system engineering, and appreciate the role of problem solving, vision, and language in understanding human intelligence from a computational perspective. The Python programming language will also be introduced.

Course learning outcomes
1. Explain the concept of tree and graph search, adversarial games, constraint satisfaction problem formulation and solving strategies, local algorithms, Bayesian networks and Bayesian reasoning, temporal probabilistic models and introduction to machine learning and results validation.
2. Implement data structures required for building intelligent agents. Student will also be able to formulate heuristics applied to real-world problem solving.
3. Evaluate and measure intelligent agent performance and formulate strategies to improve agent’s performance.
4. Develop and test data set obtained by sampling environment.
5. Undergo more advanced classes in artificial intelligence such as advanced machine learning, probabilistic reasoning and applied artificial intelligence.

This is a first course in database management systems, with coverage of physical characteristics of storage media, sequential files, external sorting, indexing: multilevel indexing and B+Trees, hashing, database design, architectures, data manipulation and query languages, database normalization and integrity, query optimization, and transaction management. This course emphasizes physical database issues, and concepts and techniques related to the entity-relationship model and relational database systems. NoSQL systems are also introduced.

Course learning outcomes
1. Define a database, and the characteristics and functions of a database management system.
2. Model simple real-world systems using (Enhanced) Entity Relationship Diagrams from a requirements specification.
3. Explain the relational data model and relational model constraints.
4. Analyze, modify, and create relational database schemas.
5. Illustrate functional dependencies and inference rules.
6. Apply normalization techniques to normalize a database up to third normal form.
7. Explain physical database storage and indexing techniques.
8. Demonstrate the use of SQL for database creation and maintenance.
9. Develop SQL queries for data aggregation, calculations, views, data manipulation, and report generation.
10. Explain transactions and their purpose, as well as the SQL syntax associated with them.
11. Design and implement a database application.
12. Express terms and concepts related to NoSQL, and its differences from the relational model.

This course introduces students to software engineering by focusing on the proven processes and techniques fundamental to the creation of reliable software systems. Software engineering is defined as the application of systematic approaches to the development and software. A disciplined approach is essential given the highly complex software required by business and consumers. In this course, students will learn the fundamentals of software engineering including software processes, requirements engineering, architectural design, implementation, software testing, and software evolution. Emphasis will be placed on the most widely used agile methods and plan-based approaches. Team based project will be used to teach how to apply software engineering processes and techniques.

Course learning outcomes
1. Explain the software lifecycle and compare major process models such as waterfall and agile.
2. Demonstrate effective software development practices through participation in a medium-sized team project.
3. Show an awareness of and facility with a full range of software tools including version control, unit testing frameworks, and build tools.
4. Explain the use case model and produce a software requirements specification.
5. Explain principles of object-oriented design and illustrate the use of software design patterns.
6. Describe the different levels of software testing (from unit to acceptance) and write test cases for non-trivial software modules.
7. Demonstrate an awareness of key ethical concerns relating to modern software systems.
8. Design systems capable of tolerating faults and recovering from failures.
9. Apply basic software project management and planning techniques.
10. Explain software quality management and the role of maintenance and evolution in the software lifecycle.

The course presents all layers of TCP/IP protocol stack, including HTTP, FTP, POP, SMTP, DNS, Peer-to-peer and Client/Server paradigms, Socket programming, TCP, UDP, Flow control, Congestion control, IP, Internet routing, Error control, Multiple access techniques, Ethernet, Wireless Networks, CSMA, CSMA/CA/CD. The course also includes topics on network security such as Public-key cryptography, Secret-key cryptography, Authentication protocols, SSL, and IPSec.

Course learning outcomes
1. Define the basic concepts of data communications, including transmission media, error detection/correction, flow control, and multiplexing.
2. Develop the types and basic concepts of wide area networks, including circuit and packet switching, routing and congestion control in packet-switched networks.
3. Analyze, modify, and create the types and basic concepts of local area networks, including Ethernet and IEEE 802.11.
4. Describe the principles of internetworking, including IPv6, routing on the internet, and the TCP and UDP protocols.
5. Design and implement simple networked applications using the socket interface in JAVA and Python.

The course describes the main components of modern computer operating systems, starting with the kernel and covers topics such as resource allocation, process scheduling, memory management, filing systems, management of input and output devices, distributed systems, multi-processor systems, synchronization, and the trend towards “virtualization”.

Course learning outcomes
1. Use a UNIX operating system as a user and as a developer.
2. Use one of the common shells (e.g. bash, csh) on the command line and in simple scripts.
3. Read, analyze, and explain complex C code.
4. Program using UNIX systems calls, in particular those for file I/O, process and thread management, inter-process communications.
5. Design and develop systems-level C programs of some significant complexity, for example: • Servers which can handle multiple simultaneous clients • Programs which are composed of multiple cooperating processes.
6. Solve synchronization problems using proper process and thread synchronization tech-niques.

Fundamentals of Finance and Entrepreneurship in two modules, thematically connected via a common case study, providing you with a solid introduction to financial statement analysis, management planning and control, corporate finance, and financial markets. The course then moves on to theories and practices of entrepreneurship and business and competitive strategy. The course has a capstone team project component, in place of a final exam

Course learning outcomes
1. Gain basic financial accounting knowledge and an understanding of its role in a business organization.
2. Comprehend the principles behind risk-return tradeoff, and its application to security valuation.
3. Comprehend basic concepts of the entrepreneurial process.
4. Comprehend basic tools for the analysis of the entrepreneurial process.
5. Apply those concepts as part of a working team in the development, reporting and presentation of a business idea.

The course introduces students to imperative programming and the development of basic algorithms using the C programming language. A detailed presentation of the language syntax will be given, along with the proper use of control structures, loops, functions, and recursion to solve problems. The creation and use of structures and arrays will also be covered, along with the development of libraries and multi-file programs.

Course learning outcomes
1. Utilize modern software development tools and methods. Such skills will be useful in students’ future computer science courses, as well as in their future careers.
2. Have proficiency in writing and explaining small to mid-sized structured programs which will incorporate fundamental language elements
3. Solve abstract computational problems through the use of programs.
4. Write their own recursive functions and analyze other’s recursive code for correctness.
5. Identify and compare common algorithms used in searching and sorting, and learn how to implement them in C.
6. Use pointers and memory management in the C language and be able to implement and explain code using these features.

Students will develop the core skills necessary to conduct research investigations and communicate those activities and results in a manner consistent with professional society guidelines and ethical practices.

Course learning outcomes
1. Identify credible sources for high-impact research papers.
2. Summarize the technical contributions of a scientific paper.
3. Compare and contrast the approaches used by different researchers to tackle a given technical problem.
4. Explain the ethical and moral requirements of conducting a research project.
5. Write a survey paper on a topic in their area of interest, comparing and integrating concepts from multiple sources.

Senior Project I & II is the first of a two-course sequence that serves as the capstone for CS majors, incorporating theory and practice from the preceding three years of coursework and internship experience. In particular, the experience of the Computer Science Seminar course exposed students to literature search and techniques required to research and author a survey paper in the field, according to professional guidelines. Following from this, the first term of the Senior Project requires the student to work (either alone or in small teams) with a faculty advisor to conduct a literature search, devise a project plan, and design the solution strategy. This will form the foundation for the actual implementation of the project in the following semester.

Course learning outcomes
1. Define a problem and a software/hardware solution to address the problem.
2. Formulate a development plan, and create the necessary requirements and design documentation for the project.
3. Develop the solution following discipline best practices.
4. Assess and evaluate the system under development.
5. Prepare and present an oral presentation and a poster, including a demonstration of the developed system.

Senior Project I & II is the first of a two-course sequence that serves as the capstone for CS majors incorporating theory and practice from the preceding three years of coursework and internship experience. In particular, the experience of the Computer Science Seminar course exposed students to literature search and techniques required to research and author a survey paper in the field, according to professional guidelines. Following from this, the first term of the Senior Project requires the student to work (either alone or in small teams) with a faculty advisor to conduct a literature search, devise a project plan, and design the solution strategy. This will form the foundation for the actual implementation of the project in the following semester.

Course learning outcomes
1. Define a problem and a software/hardware solution to address the problem.
2. Formulate a development plan, and create the necessary requirements and design documentation for the project.
3. Develop the solution following discipline best practices.
4. Assess and evaluate the system under development.
5. Write a technical paper about the project (CSCI 409 only).
6. Prepare and present an oral presentation and a poster, including a demonstration of the developed system.

This course is designed to train students in printed circuit board (PCB) design and manufacturing. PCB design is an integral part of each electronics products and this is a basic course to help students to design their own PCB projects. This course is designed to teach students how to design and layout PCBs for various electronics applications by giving students a comprehensive understanding of the design and fabrication of PCBs. The course includes theoretical and practical training, covering topics such as digital electronics, analog circuits, schematic capture, PCB layout, and design for manufacturability. The course also includes hands-on design projects, where students can design and fabricate their own PCBs using industry-standard design software and fabrication tools. They are also trained in using 3D design tools to visualize electronic components and circuits. The course not only touches on the technical aspects but also teaches students about the aesthetics of PCB design, including printed circuit board layout, various routing techniques, and proper placement of components. At the end of the course, students will have a good understanding of PCB design principles and practical experience designing and prototyping their own PCBs.

Course learning outcomes
1. Understanding the fundamentals of PCB fabrication and design.
2. Basic placement and routing strategies for single and double-sided designs.
3. Determine appropriate components to make circuits.
4. Drilling and Etching Process.
5. Interpret test results and measurements on electric circuits.
6. Analyze the fabrication processes of PCBs.
7. Apply the software and hardware for PCB Design.

This course covers fundamental technologies of mobile information systems and wireless communication networks. Topics of study include characteristics of the mobile radio environment, propagation phenomena, cellular concept and channel allocation, dynamic channel allocation and power control, multiple access techniques: FDMA, TDMA, CDMA – system capacity comparisons. Several standard protocols for wireless networks (DAB, DVB, LTE, GSM, 802.11, 802.15 etc.) are covered. The is focus on qualitative descriptions and realistic explanations of relationships between wireless systems and performance parameters. Mobility support in network and transport layers is also included.
The main course aims are as follow:

• Introduces wave propagation characteristics and models.
• Provides students with up-to-date knowledge of the techniques and the principles underlying cellular network design.
• Introduces different wireless communication techniques and standards.
• Provides students skills in traffic engineering

Course learning outcomes
1. Explain the wireless data transfer and propagation characteristics of wireless channels.
2. Define cellular network architecture with functionalities of different elements.
3. Define multiple access technologies and frequency reuse, interference, traffic capacity and coverage area estimation in cellular network.
4. Formulate basic queuing systems and apply them to cellular networks.
5. Interpret the current and upcoming wireless standards in terms of their spectral, power efficiencies.
6. Explain and analyze diversity techniques and apply it to enhance the performance of wireless systems.
7. Emphasize the social and economic impact of wireless communication systems.

This course provides material about controlling industrial processes and automating the production lines of the factories. Topics include the definition of industrial automation, an overview of the Factory Automation, programmable logic controllers (PLCs), PLC hardware components, PLC programming using Ladder logic, programming timers and counters, PLC installation, networking, Human machine interface (HMI), industrial sensors and actuators, function block diagram programming, structured text programming, sequential function chart programming, continuous function chart programming. CODESYS software will be used for simulating the PLC and HMI to practice programming. Siemens s7-1212C AC/DC/RLY together with Siemens totally integrated automation (TIA) platform will be used to practice PLC programming in the lab to get hands-on experience. Programming with LabView to have the supervisory control (SCADA) and some advanced topics in industrial automation will be considered as additional possible topics to be covered.

The main course aims are as follow:
1) The students will gain proper knowledge about the concept of the industrial automation
2) The students will become familiar with the state-of-the-art tools and techniques used in the field
3) The students will be able to compare several available tools and techniques with each other and pick the one which suits better the given problem
4) Students will practice working in a team for their course projects
5) The students will apply the gained knowledge in the course to design, simulate and implement a typical industrial automation system

Course learning outcomes
1. Define industrial automation and the concepts behind it.
2. Program a PLC using the provided software and the determined programming language.
3. Apply and integrate knowledge in practice (via term project).

ELCE 308 is the first course in communication engineering that convers the concepts both in analog and digital communication systems including amplitude (AM) and frequency modulation (FM), pulse code modulation (PCM), phase shift keying (PSK)and quadrature amplitude modulation (QAM). Analog to digital conversion methods, sampling and quantization, are briefly revisited. The effects of noise and the transmission medium on the system performance through signal-to-noise ratio (SNR), intersymbol interference (ISI) and bit-error-rate (BER) analysis are discussed. For digital communication, optimal receivers with matched filtering for both baseband and passband transmission are discussed In detail. Fourier transform techniques, linear systems, filtering, power and energy spectral density of random signals are reviewed for conceptual purposes. Laboratory and software assignments are given to exercise the concepts and techniques covered in class.

Course learning outcomes
1. Analyze amplitude and angle modulation schemes.
2. Practice analog to digital conversion through sampling and quantization.
3. Analyze baseband and passband digital modulation schemes.
4. Use modern hardware and simulation tools for evaluating and presenting the performance of communication systems.
5. Describe the role of communication systems in technology, culture and society.

This course is designed to familiarize students with the fundamental concepts in digital signal processing (DSP).

A list of topics includes:

• Introduction to DSP •Review of signals and systems (convolution vs Correlation).
• Fourier domain analysis, and Discrete Fourier Transform (DFT), Properties of DFT, Circular Convolution, linear vs circular convolution.
• Z-Transform and its applications in signal processing. •Sampling & digital processing of continuous time signals.
• Transform-domain analysis of linear time-invariant (LTI) systems. •Structures for implementation of digital filters.
• IIR Digital filter design & FIR filter design. Overview of Signal Processing Applications

Course learning outcomes
1. Explain the basics of discrete-time signals and systems, and sampling and sampling-rate alteration.
2. Perform transform-domain analysis using z-Transform and discrete Fourier transform.
3. Design digital (FIR and IIR) filters, and develop various structures for their realization.
4. Apply and Integrate knowledge in practice via numerical/experimental study (MATLAB/Simulink/DSP Kits).

The objective of this course is to introduce fundamental properties of linear systems and transform techniques to analyze the behavior of linear systems. Students are also expected to gain an appreciation for the importance of linear system theory in electrical engineering.

A tentative list of topics includes:

• Introduction to signals: classifications, transformations, and basic building-block signals.
• Introduction to systems: properties (linearity, time-invariance, causality, etc.) and system interconnections.
• Time-domain analysis: convolution sum and convolution integral, linear constant-coefficient difference and differential equations.
• Frequency domain analysis: Fourier series (derivation, properties, and convergence), Continuous-time Fourier transform (derivation, properties, convergence), Discrete-time Fourier transforms (derivation, properties, convergence). Laplace transform (properties, convergence), inverse Laplace transform.
• Introduction to Z transform (time-permitting).

Course learning outcomes
1. Describe the classifications and Perform basic manipulations of signals and systems.
2. Analyze LTI systems in time-domain using convolution as well as differential/difference equation representation.
3. Describe the Fourier-series representation for periodic signals and Perform frequency-domain analysis of periodic signals using the Fourier series.
4. Explain the transform-domain analysis based on Fourier and Laplace transforms, and Analyze signals and systems in these domains.

This course presents the introductory concepts that are needed in order to design digital systems including combinational and sequential digital logic, and state machines. Concepts of Boolean algebra, Karnaugh maps, flip-flops, registers, and counters along with various logic families and comparison of their behavior and characteristics are covered. Additionally this course presents an introduction of the Hardware description language (VHDL) and introduce students to design combinational and sequential circuits using VHDL and simulators.

Course learning outcomes
1. Describe the behavioral functionality of digital circuits in an HDL.
2. Create gate-level implementation of combinational and sequential logic functions.
3. Solve basic binary mathematical operations related to binary logic gate operations.
4. Design, analyze, and troubleshoot digital circuits.
5. Create a state transition diagram from a description of a sequential logic function.
6. Use modern digital design tools.

The field of Human-Robot Interaction (HRI) is a significant area of research in robotics. This interdisciplinary research area is concerned with understanding, designing and evaluating robotic systems used by or with humans. This course is for students who seek a background and hands-on experience in this interdisciplinary field comprising of robotics, artificial intelligence, computer science, human-computer interaction, cognitive psychology and others. During this course, we will discuss what social and technical issues apply to the design of HRI systems that will be practiced in a special term project resulting in the implementation of a complete HRI system. In addition, we will learn how to develop and conduct experiments, analyze and interpret data. This project may be developed using a robotic platform from NU robotics labs.

Course learning outcomes
1. Demonstrate knowledge related to HRI research and applications.
2. Design and implement complete HRI systems taking into account ethical, safety and privacy issues in social settings.
3. Develop and conduct HRI experiments, analyze and interpret obtained data.
4. Work as a part of a team on HRI systems developing creative thinking and communicative skills.

ROBT 407, Machine Learning with Applications, introduces students to advanced analytical tools and techniques used in modern machine learning. This 6-credit course emphasizes both the theoretical foundations and practical implementations of machine learning models, preparing students for complex, real-world challenges in engineering and robotics. Key topics include supervised, unsupervised, and semi-supervised learning; neural networks; support vector machines; linear and logistic regression; kernel-based learning methods; and deep learning techniques such as convolutional and recurrent neural networks, transformers. Students will gain hands-on experience with data preprocessing, feature engineering, model training, and evaluation through integrated term projects using industry-standard programming tools like Python, MATLAB, and C++. The course also covers critical issues such as model generalization, overfitting, and regularization, equipping students with the skills to design robust machine learning solutions. By the end of the course, students will be proficient in using machine learning algorithms to address interdisciplinary problems in robotics, autonomous systems, and beyond.

Course learning outcomes
1. Demonstrate comprehensive theoretical knowledge of machine learning methods, including supervised, unsupervised, and semi-supervised learning approaches used in engineering applications.
2. Design and optimize machine learning pipelines for feature extraction, classification, regression, and clustering tasks using state-of-the-art algorithms and evaluation techniques.
3. Implement machine learning solutions using industry-standard programming tools (Python, MATLAB, C++) and apply them to practical data-driven problems in engineering and robotics.
4. Apply advanced machine learning methods — including deep learning, convolutional and recurrent neural networks — to analyze complex datasets and solve interdisciplinary problems in autonomous systems and beyond.

This course provides an introduction to the fundamental concepts, algorithms, methods and tools of digital image processing. The course aims to lay a solid mathematical foundation to further study of concepts in image and video processing, computer vision, image segmentation and understanding. After studying the basics of image formation and human visual system, topic such as image sampling and quantization, intensity transformations, spatial filtering, filtering in the frequency domain, color image processing, image processing and image segmentation will be covered. Matlab and OPENCV (using Python or C++ programming languages) will be used extensively for projects which aim to provide practical insight into the real-world implementation of image processing techniques. The course will have a team-based final project which requires the students to learn topics and methods beyond the course material.

Course learning outcomes
1. Describe and explain basic principles of digital image processing.
2. Design and implement algorithms that perform basic image processing (e.g., histogram processing, noise removal, filtering and image enhancement).
3. Analyze 2D signals in the frequency domain through the Fourier transform.
4. Acquire an appreciation for the real-world image processing issues and constraints.
5. Conduct independent study and analysis of image processing problems and techniques.

A survey of sampled data systems and embedded architecture; key concepts in common embedded system applications; signal processing and control; embedded microprocessor and device interface; time-critical I/O handling; data communications; real-time operating systems and techniques for the development and analysis of embedded real-time software; hands-on laboratory projects. A structured project is required. Prerequisites: Microcontrollers w/ Lab

Course learning outcomes
1. Describe embedded microprocessor architecture, peripheral interfacing and embedded operating system principles.
2. Work in a Linux operating system running on a personal computer PC and an embedded hardware and program multitasking applications in C/C++ language.
3. Interface analog and digital sensors to a system-on-chip microprocessor board via various communication protocols.
4. Work in a part of the team on embedded system design for engineering problems developing creative thinking and communicative skills.
5. Self-study and work independently on engineering projects.

The course presents and integrates the basic concepts for both continuous-time and discrete-time signals and systems. Signal and system representations are developed for both time and frequency domains. These representations are related through the Fourier transform and its generalizations, which are explored in detail. Filtering and filter design, modulation, and sampling for both analog and digital systems are discussed and illustrated. Main concepts shown in class are concretized through labs focusing on different sensors such as accelerometers, gyroscopes, magnetometers, capacitive touch sensors, incremental encoders, potentiometers, microphones, etc… The sensor data will be acquired using data acquisition cards and will be analyzed using MATLAB software.

Course learning outcomes
1. Analyze continuous-time and discrete-time signals and systems in the frequency domain using mixed-signal classes.
2. Discuss the concept of sampling and the concepts that link continuous-time and discrete-time signals and systems, also using MATLAB and laboratory experiments to simulate and to analyze signals and systems for this situation.
3. Discuss the effect of system poles and zeros on the frequency responses of systems.
4. Analyze discrete-time signals and system responses using the concepts of transfer function representation by use of Z and inverse-Z transforms.
5. Apply time-domain and frequency-domain analysis tools to design analog and digital filters, also using MATLAB and laboratory experiments for applications of filters.

In this course students will acquire the basic skills for solving physics problems numerically. Possible computational tasks will involve graphing, finding roots, numerical interpolation, extrapolation, numerical differentiation and integration, numerical diagonalisation of matrices as well as numerical simulations of physical phenomena governed by differential equations.

Course learning outcomes
1. Identify suitable computational methods for solving a given mathematical problem.
2. Design and develop a code for solving a given computational problem.
3. Perform detailed tests and verifications of a code.
4. Analyze the advantages and disadvantages of various numerical techniques.
5. Perform in-depth analysis and make insightful plots of the data.
6. Write reports in a scientific style.

The course is an introduction to modern cryptographic algorithms and to their cryptanalysis, with an emphasis on the fundamental principles of information security. Topics include: classical cryptosystems, modern block and stream ciphers, public key ciphers, digital signatures, hash functions, key distribution and agreement.

Course learning outcomes
1. Understand classical and modern approaches to encryption.
2. Understand public-key cryptography.
3. Understand the difference between absolute and computational security.
4. Perform some simple cryptanalysis under various assumptions.

This course is a one-semester course intended for mathematics, computer science students. Thiscourse will cover the fundamental concepts of Graph Theory: simple graphs, digraphs, Eulerianand Hamiltonian graphs, trees, matchings, networks, paths and cycles, graph colorings, andplanar graphs. Famous problems in Graph Theory include: Minimum Connector Problem(building roads at minimum cost), the Marriage Problem (matching men and women intocompatible pairs), the Assignment Problem (filling n jobs in the best way), the Network FlowProblem (maximizing flow in a network), the Committee Scheduling Problem (using the fewesttime slots), the Four Color Problem (coloring maps with four colors so that adjacent regions havedifferent colors), and the Traveling Salesman Problem (visiting n cities with minimum cost).

Course learning outcomes
1. Interpret graphs both as mathematical objects, data structures, unifying abstraction of natural and computing systems.
2. Discuss a variety of graph properties and their interrelatedness.
3. Apply skills acquired in discrete mathematics, linear algebra and/or computation to study graph properties.
4. Formulate solutions to a problem arising in the real-life, science, or engineering related to graph theory in team setting.

This course is designed to teach students basic skills that are essential in research work in mathematical sciences. Specific topics include: preparing scientific manuscripts and scientific reports using modern document preparation system LaTeX along with BibTeX and packages for creating professional graphics; preparing technical oral presentations using LaTeX and PowerPoint; efficient literature search and tools for literature review; gathering and critical evaluation of scientific information; characteristics of good research; science ethics; research funding; research proposals; bibliometrics and scientometrics; publication process in peer-reviewed journals; dissemination of research results. Along with the general concepts relevant to those topics, the students will learn how to explore research opportunities in the general mathematical sciences. Also, students will acquire skills useful for preparing resumes and job applications. The regular classes will be enriched by computer laboratory sessions and professional presentations of on-going research in the Math Dept and invited talks.

Course learning outcomes
1. Solve numerically mathematical problems (including root finding, interpolation, integration, differentiation, and ordinary differential equations).
2. Implement numerical algorithms using a programming language, i.e., Matlab.
3. Apply the fundamental principles of mathematical approximations and the concept of approximations errors.
4. Apply the concept of forward and backward errors.
5. Analyze approximation errors and construct some bounds of errors.
6. Perform basic convergence analysis of an approximation method.

The course starts where Probability ends and considers point estimation, method of moments, maximum likelihood estimation, unbiasedness, consistency, sampling distributions, confidence intervals, hypothesis testing, power of tests. Categorical data inference may be covered if time permits.

Course learning outcomes
1. Understand the difference between probability and likelihood functions, and compute the maximum likelihood estimate for a model parameter.
2. Compute confidence intervals for parameter estimates.
3. Use null hypothesis significance testing to test the significance of results and compute the p-value for these tests.
4. Apply specific significance tests including, z-test, t-test (one and two samples), and chi-squared test.

System analysis and design is about business problem solving and computer applications. The information systems field is focused on the analysis of an organization and the subsequent design of solutions to meet business requirements. A disciplined approach is important given the highly complex software required by business and consumers. In this course, students will learn the fundamental concepts and trends that provide the context of system analysis and design methods. Students will learn and follow a structured process called the systems development life cycle. Students will learn to identify and solve business problems, concepts such as the software lifecycle, process models, requirements engineering and design, along with past and current issues in the field. Students will practice the discipline of system analysis and design by analyzing a hypothetical case situation. Team based exercises will be used to teach system analysis and design skills. Students will be exposed to standard tools from industry.

Course learning outcomes
1. Explain the principles of system analysis and design.
2. Describe the role of systems analyst and business analyst.
3. Structure system process requirements.
4. Explain methodologies and the differences among them.
5. Practice effective system analysis and design practices through participation in a medium-sized team project and work successfully with a group of your peers on a common problem.
6. Explain the use case model and produce software requirements specifications.
7. Initiate, plan and organize systems development projects.
8. Design Information Systems and Interfaces.

In this course, students will learn the fundamentals of software project management methodologies, professional practices, including concepts such as the software lifecycle, process models, requirements engineering, and design, along with past and current issues in the field. A disciplined approach is important given the highly complex software required by business and consumers. Emphasis will be placed on traditional software project management methods and learning about project planning and management issues, including team organization and control, scheduling and tracking, risk analysis and mitigation, quality assurance, management and complexity metrics, along with tools for aiding in the project management task. Students will be exposed to standard tools from industry. The PMBOK will be used, providing essential project management knowledge. Team based exercises will be used to teach project management skills.

Course learning outcomes
1. Describe the role of the software project manager, the organizational structure, and key personnel positions associated with planning and managing software development and maintenance activities
2. Explain the software lifecycle and compare major process models such as Waterfall and Agile.
3. Demonstrate effective software project management practices through participation in a medium-sized team project. Work successfully with a group of your peers on a common problem.
4. Explain the use case model and to produce a software requirements specification.
5. Estimate software development size, effort, and schedule for new program proposals or enhancements to existing programs. Staff the software engineering organization and orienting new employees.
6. Plan and document software development project plans. Manage cost and schedule.
7. Assess and control risks.
8. Continuously improving processes and the role of the software manager.
9. Evaluate performance.

This course is concerned with a broader scope of issues, topics and methods than traditional human computer interaction (HCI), with an interdisciplinary flavour ranging from cognitive science and interaction design aspects, within and beyond computer science, to software engineering and formal methods approaches to modelling and verification. The number of different types of interactive systems available today has increased steadily in the past decade, so this course, likewise, has been expanded to cover this. During this course, we will discuss a wide range of models of interactive systems, how cognitive, social and emotional issues apply to interaction design, and how to incorporate human-computer interaction aspects within the software process development.

Course learning outcomes
1. Summarize the various aspects of HCI as an interdisciplinary area.
2. Effectively use techniques for data gathering, analysis, requirement elicitation, design and evaluation and apply them to interaction design using practical case studies.
3. Explain how to involve stakeholders in the design process.
4. Explain the foundations of human memory, perception and cognition.
5. Articulate the principles of formal modelling and verification and their relevance to the analysis of non-functional properties in general as well as specifically to safety and usability.
6. Use technologies to design engaging and enjoyable rather than frustrating interactive systems.
7. Effectively use techniques for abstracting, modelling and analyzing interactive systems and to apply them to practical case studies.

This course provides undergraduate students with the opportunity to gain experience beyond the classroom through an industry or research-based internship. Through supervised work and structured reflective activities, students develop essential professional and technical skills, expand their professional networks, and gain insight into their chosen field. The course is designed to enhance students’ preparedness for employment or further academic study following graduation.

Course learning outcomes
1. Identify and carry out performance objectives as determined by their on-the-job supervisor.
2. Develop habits and attitudes necessary for job success.
3. Better assess their own interests and abilities in relation to their field of study.
4. Reflect and assess their work experiences via written weekly logs and a final presentation.

This course provides undergraduate students with the opportunity to gain experience beyond the classroom through an industry or research-based internship. Through supervised work and structured reflective activities, students develop essential professional and technical skills, expand their professional networks, and gain insight into their chosen field. The course is designed to enhance students’ preparedness for employment or further academic study following graduation.

Course learning outcomes
1. Identify and carry out performance objectives as determined by their on-the-job supervisor.
2. Develop habits and attitudes necessary for job success.
3. Better assess their own interests and abilities in relation to their field of study.
4. Reflect and assess their work experiences via written weekly logs and a final presentation.

This course provides undergraduate students with the opportunity to gain experience beyond the classroom through an industry or research-based internship. Through supervised work and structured reflective activities, students develop essential professional and technical skills, expand their professional networks, and gain insight into their chosen field. The course is designed to enhance students’ preparedness for employment or further academic study following graduation.

Course learning outcomes
1. Identify and carry out performance objectives as determined by their on-the-job supervisor.
2. Develop habits and attitudes necessary for job success.
3. Better assess their own interests and abilities in relation to their field of study.
4. Reflect and assess their work experiences via written weekly logs and a final presentation.

This course is intended for students interested in the efficient use of modern parallel systems ranging from mobile multi-core to server/ cluster many-core systems. Topics such as parallel computer architectures, the programming models, memory hierarchy, parallel program design and parallel programming tools for multi-core systems and general-purpose graphics processing units (GPGPUs) will be covered. In the second part, the course covers the most common and current GPU parallel programming techniques with lab-based programming assignments using CUDA API

Course learning outcomes
1. Define concepts related to CPU and GPU hardware architectures from mobile multi-core to server/ cluster many-core parallel systems.
2. Articulate the differences of diverse parallel systems with the connection of parallel programming tools/APIs
3. Critically assess the performance of various CUDA applications, utilizing both quantitative metrics and qualitative insights, to identify the bottlenecks
4. Design and implement parallel programs using the parallel programming techniques and CUDA
5. Integrate current research findings, methodologies, and advancements into heterogeneous computing

This is the course on the foundations mobile and ubiquitous computing. We will learn the fundamentals and acqire hands-on experience with mobile computing and sensors and actors of current mobile and wearable devices, like front and back camera, microphone, speaker, accelerometer, NFC, touchscreen, or external sensors. We will learn mobile application development and corresponding mobile GUI design. To extend the computational abilities and connectivity, we will develop and deploy simple cloud services. We will further study human computer interaction with a focus on touch-based interfaces, discuss and apply location based services, and analyze online payment systems. For the course projects, we will learn and apply agile programming concepts and development processes. Open for non-majors.

Course learning outcomes
1. Implement common types of mobile applications on a given development platform.
2. Explain the inherent tradeoffs, constraints, and limitations of mobile computing platforms.
3. Compare and contrast unique features offered by mobile platforms including location-awareness, context sensitivity, and sensing.
4. Describe and explain emerging trends and challenges in mobile computing such as application areas and privacy.

This course will introduce students to the fundamentals of wireless sensor networking, from design to deployment. The design of wireless sensor networks will be covered at all levels of abstraction from the physical layer up to the application layer. Topics will include wireless signals and propagation, networking protocols, power management, data acquisition and processing, operating systems, and sensor modality an design. The course will cover major industrial and healthcare application as well as related topics in pervasive computing such as the Internet of Things. Students will balance the theory with practice, gaining hands-on experience through team-based projects.

Course learning outcomes
1. Explain the principles of sensor networks and ad hoc networks.
2. Develop efficient MAC, routing, information dissemination protocols for sensor networks.
3. Assess and develop privacy providing protocols for sensor networks.
4. Articulate existing challenges in sensor networks.
5. Develop simple sensor network applications using sensor boards and devices.

Introduction to data-mining techniques, including data preprocessing, data-mining primitives, association rules, decision trees, cluster analysis, classification and machine learning, data visualization, and data warehousing. Detailed applications from a wide variety of domains are analyzed with attention toward algorithm selection.

Course learning outcomes
1. Appraise a variety of supervised and unsupervised learning algorithms and to select an appropriate algorithm for a given problem.
2. Assess the efficacy of data mining techniques quantitatively.
3. Write effective software and scripts to process datasets via individual assignments and team projects.
4. Articulate some of the ethical and philosophical implications of data mining technology.

During the course, the student will learn how high-level programming languages are translated into executable, low-level code. The course covers the complete process of: (1) Lexical analysis, which separates program text into keywords, identifiers and operators; (2) Parsing, which constructs an abstract tree representation (AST) of the program; (3) Type checking, which resolves overloaded names, and inserts implicit conversions; and (4) Lowering, which converts the resulting AST structures into an intermediate representation. The final part of the course discusses optimization through such means as detecting constants and eliminating redundant computations.

Course learning outcomes
1. Use tools for lexical analysis and recognize situations where they can be effectively applied.
2. Use tools for parser generation and be aware of their possibilities and limitations.
3. Explain how type checking works, and how it could be implemented.
4. Implement lowering of ASTs into an intermediate representation and understand how to approach stack-based evaluation.
5. Summarize the basics of compiler optimization, such as the alias problem. constant propagation, and redundancy elimination.

The overall goal of this course is for students to develop their algorithmic problem-solving skills. Instead of theory, the focus will be on practice and implementation. Students will cover fundamental and advanced algorithms, as well as advanced data structures which are not usually covered in standard algorithms classes. We will complement these with strategies on how to efficiently implement solutions in C++ or Java. In addition, students will be trained to dissect, analyze, and solve real world problems and tasks under various constraints mimicking real life conditions in industry.

Course learning outcomes
1. Choose the most appropriate data structures for solving algorithmic problems, augment those data structures with nontrivial properties, and implement them efficiently.
2. Solve advanced combinatorial optimization problems using techniques such as Dynamic Programing, Parametric Search, etc.
3. Use advanced problem-solving techniques and algorithms for graphs and strings.
4. Use standard and advanced algorithms in Number Theory and Computational Geometry.
5. Communicate algorithmic solutions clearly.
6. Solve challenging algorithmic problems efficiently within realistic time constraints.

This course aims at providing a comprehensive study of the principles and practices of information security, covering a broad selection of key challenges when working with computer system security including software security, operating system security, database and cloud security. The wide spectrum materials to be covered include security concepts and services, access control, introduction to cryptography, public key infrastructure, and IT security management and control. The basic information security objectives such as data integrity, identification, user authentication, authorization, validation, denial-of-service and access control are examined.

Course learning outcomes
1. Explain and articulate key information security principles regarding information, confidentiality, integrity, and availability.
2. Critique and assess the strengths and weaknesses of general cybersecurity models, including the CIA triad.
3. Describe principles of incident response and incident management.
4. Explain the usage of common key cryptography and public key cryptography.
5. Assess the role of strategy, access control, and policy in determining the success of information security.
6. Evaluate the trends and patterns that will determine the future state of cybersecurity.

This course introduces students to the fundamentals of the Internet of Things. The Internet of Things consists of devices which can sense their environment using sensors, can sometimes collaborate with each other, and report their findings through radio communication protocols. These interconnected devices offer exciting new services to improve people’s quality of lives and can be applied to various application domains such as smart cities, smart homes, smart agriculture, and industrial automation. In this course, an overview of the societal benefits of the Internet of Things is provided along with the basic understanding of the main components of Internet of Things networks at all levels of abstraction from the physical layer up to the application layer. Topics include smart sensing, wireless communications and propagation, media access control layer protocols, routing protocols, and application layer protocols. The course balances the theory with practice introducing hands-on exercises, small team projects, and project presentations.

Course learning outcomes
1. Summarize the societal benefits of IoT and its applications.
2. Demonstrate knowledge of radio communication fundamentals as well as major MAC, routing, and application layer protocols and IoT cloud services.
3. Choose major radio technologies for IoT according to the application scenario.
4. Develop simple IoT network applications using sensor boards and connect them to the cloud.

This course covers computational techniques for mining the large amount of information produced by recent advances in biology, such as genome sequencing and microarray technologies. Main topics of the course include: DNA and protein sequence alignment, phylogenetic trees, protein structure prediction, motif finding, microarray data analysis, gene/protein networks.

Course learning outcomes
1. Explain the main computational problems in life sciences.
2. Describe the main terminology used in bioinformatics.
3. Perform statistical analyses on results of algorithms.
4. Describe the key methods and tools used in bioinformatics.
5. Design and implement a computational solution to a molecular biology problem.

During this course students will be introduced to a variety of machine learning algorithms and techniques. Particular emphasis will be placed on understanding the theoretical basis. By the end of the course, students will be able to derive most commonly used methods mathematically from first principles and therefore have a firm grasp of the underlying theory. In addition, students will also be asked to implement various machine learning techniques from scratch in python during coding tutorials and thus also learn practical applications of the theoretical concepts.

Course learning outcomes
1. Apply the proper mathematical techniques to the machine learning task.
2. Derive a whole range of machine learning techniques from first principles.
3. Explain and defend their theoretical basis and assumptions these may have.
4. Implement a range of machine learning techniques themselves in python from scratch.
5. Identify appropriate techniques for a given dataset.
6. Validate their approach, e.g. by means of cross-validation.
7. Explain the bias-variance trade-off (i.e. overfitting / underfitting).

Scripting languages are widely used in industry and research environments for high-level automation and rapid prototyping. This course introduces scripting through practical case studies and will cover a variety of languages of languages and environments including: JavaScript, Perl, shell scripting, php, Lua, and Python. Illustrative applications include data analysis, web programming and development, system administration, user interfaces, and text processing. Additionally, computing concepts such as interpreters and type systems are discussed in the context of scripting languages.

Course learning outcomes
1. Explain the design decisions of programming languages and systems and to identify the practical consequences of those decisions.
2. Develop software effectively and idiomatically in a variety of scripting languages including Javascript, Bash, and Python.
3. Explain how scripting integrates with current industrial best practices.

The course will begin with an introduction to the history of open source software (OSS), the organization of OSS communities, and the software development models and support tools used by such communities. Students will then explore a number of active OSS projects, and each will become an active participant in one with a specific role to be carried out throughout the semester.

Course learning outcomes
1. Explain the OSS phenomenon and its impact on software industry and society.
2. Explain the ethics and philosophy of collaboration that rule in OSS communities.
3. Assess an OSS project in terms of its evolution, status and community dynamics.
4. Argue the strategic advantages and business perspective of the OSS model.
5. Comparatively evaluate the quality of OSS products and apply the appropriate criteria for adoption and ruse.
6. Actively participate in an OSS community either as a direct contributor, critical observer, or adoption expert.

During this course students will learn about the most important topics of Brain Computer Interfaces (BCI). Students will study basic neuroanatomy, neurophysiology as well as the neural basis of Electroencephalography (EEG). Event-Related Potentials (ERP), multi-variate ERP models as well as linear models of EEG will be introduced. Furthermore, some common data analytical tools will be covered, such as PCA, NCC, LDA/ RLDA. Also, ERP as well as SMR-based BCI applications and SSVEP-based BCI will be introduced. At the end of the course, students will carry out a small project.

Course learning outcomes
1. Describe basic concepts of neuroanatomy and neurophysiology.
2. Explain the neural basis of EEG and other neuroimaging techniques, such as NIRS and fMRI.
3. Assess which different neural correlates can be exploited for BCI.
4. Evaluate and implement a range of data analytical techniques, that are common to BCI in particular, but also in EEG and neuroscientific research in general.
5. Design and conduct a small BCI study, i.e. experimental design, implementation, conduction, data analysis, report writing.

This one semester course, which is intended for students majoring in Computer Science, introduces students to the fundamental concepts of deep learning. The main themes of the course are benefits, properties and challenges of deep learning; introduction to machine learning and optimization; challenges and common approaches; logistics regression; gradient descent; perceptron learning; stochastic gradient descent; multi-layer perceptrons; non-linearity in ANNs; representational capacity; bias-variance dilemma; overfitting, generalization; regularization; convolutional neural networks; restricted Boltzmann Machines; deep recurrent networks and sequence learning; applications to pattern recognition from video data, speech recognition and natural language processing. The students will learn how to design a deep learning architecture. The course also explores several common deep learning techniques and exposes the students in how to use deep learning efficiently. Students will gain experience through assignments, with an emphasis on proper deep learning practices with appropriate applications.

Course learning outcomes
1. Develop solutions using deep learning with Python programming language and environments to solve problems and perform specific tasks, using proper hyper parameters and optimization techniques.
2. Explain and apply concepts related to deep learning, such as logistics regression; gradient descent; regularization; Convolutional Neural Networks; Deep Recurrent Networks; Attention Mechanism; Transformer Architecture.
3. Explain and illustrate the strengths and weaknesses of various deep learning approaches.
4. Critically determine how to select a deep learning approach for a domain and task.

This course will examine the fundamental software testing, development process, test driven development and related techniques. In particular, the important phases of testing will be reviewed, emphasizing the significance of each phase (Unit testing, Integrations testing, System testing, and Acceptance testing) when testing software projects. The course will also include concepts such as test case generation, metrics, test coverage, regression testing, mutation testing, mobile testing, agile testing, program analysis (e.g., program-flow and data-flow analysis), cyclomatic complexity, and test prioritization. The course balances the theory with practice introducing hands-on exercises, small team projects, and project presentations.

Course learning outcomes
1. Summarize the societal benefits of Software Testing and its applications.
2. Applying different testing strategies, techniques and tools to software projects.
3. Explain various Software Quality Assurance (SQA) fundamental concepts, definitions, objectives and activities performed during the development and maintenance of software systems.
4. Learn software quality management components such as quality metrics and software quality costs.

Since its emergence in the 1950s, the discipline of natural language processing has been concerned with building computer systems capable of understanding various aspects of human language. Natural language processing enables a wide range of modern applications such as machine translation, information retrieval, virtual assistants, etc. Recently, we have witnessed impressive progress in the discipline thanks to the application of neural networks, especially transformer-based architectures. This course is intended for students interested in the theoretical foundations of natural language processing and gaining practical experience in building such systems. During the course, we will discuss a variety of tasks, approaches, and tools, accompanied by a series of practical assignments covering common natural language processing tasks.

Course learning outcomes
1. Demonstrate knowledge related to the main approaches and methods in Natural Language Processing.
2. Apply existing tools and frameworks to solve Natural Language Processing tasks.
3. Design and perform Natural Language Processing experiments, analyze, evaluate and report obtained results.

This course is a one-semester course intended for undergraduate students. Students will gain a deep understanding of pivotal topics, including the intricacies and innovations of recent artificial intelligence architectures. The course will cover diverse generative models such as encoder-decoder architectures, contrastive learning, seq2seq models, attention/ transformer mechanisms, diffusion models, and a variety of specialized loss functions. This comprehensive overview will equip students with a broad understanding of the key approaches in generative modeling. Students will engage in practical, hands-on projects utilizing real-world generative models to tackle contemporary challenges and applications, including computer vision, speech and natural language processing, graph mining, reinforcement learning, and trustworthy machine learning. In this course, students will be introduced to extensive, large-scale datasets pivotal to the field of Generative Artificial Intelligence. All practical components will be delivered using the Python programming language, leveraging the power and versatility of leading libraries, including TensorFlow, PyTorch, and Scikit-learn.

Course learning outcomes
1. Demonstrate a thorough understanding of the foundational principles and advanced concepts underpinning generative models and their associated architectures.
2. Apply knowledge of deep learning architectures, such as encoder-decoder structures, diffusion models, seq2seq, attention mechanisms, transformers, BERT, and GPT.
3. Critically assess the performance of various generative models, utilizing both quantitative metrics and qualitative insights, to determine their suitability for different applications.
4. Integrate current research findings, methodologies, and advancements into generative model development and problem-solving.
5. Exhibit the ability to independently explore, understand, and integrate new tools, techniques, and research developments in generative AI, demonstrating a commitment to lifelong learning.

The course covers the design and analysis of algorithms, and investigates the practical and theoretical limits of computing. We will investigate classic algorithmic approaches to solve computational problems, and the use of asymptotic analysis to quantify their efficiency. In addition, we will discuss the concepts of computational intractability, NP-completeness, Turing machines and computability.

Course learning outcomes
1. Explain the Church-Turing thesis and its significance.
2. Explain the difference between decidable, recognizable, and unrecognizable languages.
3. Be able to determine which class a language may belong to using methods such as Rice’s Theorem and mapping reductions.
4. Define the complexity classes P and NP, and give examples of each.
5. Define and explain the significance of NP-completeness, and give examples of such problems.
6. Prove that a problem is NP-complete using reductions.
7. Define space complexity and the class P-SPACE, and understand its relationship with other complexity classes.