Master of Science in Data Science

This course is a one-semester course intended for graduate students in Data Science graduate programs. It is designed to introduce object-based programming and programming concepts with Python, Algorithm analysis and design, basic data structures, and Scientific computation and visualization with Python. The course will include the following sub topics; Introduction to Python and its basic data types; Object-oriented programming with Python; Recursion; Algorithm analysis; Searching and sorting; Abstract data types such as linked lists, stacks, queues, priority queues, trees, hashes and their implementation in Python; Vectors and matrices in Python; Plotting and visualization with Python; Solving systems of linear equations with Python. Students will gain experience through assignments and projects, with an emphasis on proper data science practices with appropriate applications. Outline of major topics: 1) Introduction to the Python interpreter, data types, and container data types, control structures using sequential, iterative and conditional execution; 2) functions, scopes and namespaces, recursion; 3) object-oriented programming with Python; 4) algorithm analysis, time and space complexity, basic notations, analyzing complexity of algorithms; 5) searching and sorting, linear search, binary search, bubble sort, selection sort, insertion sort, quicksort; 6) abstract data types, linked lists, stacks, queues, priority queues, trees, hashing and their implementation in Python; 7) towards data science and engineering with Python - vectors and matrices in Python; plotting and visualization with Python; solving systems of linear equations with Python; data storage; data analysis [All these items based on Numpy, Pandas, Scipy libraries].
Source note: the Program Specification Report contains an additional sentence in the DS 501 description that is visibly truncated in the original PDF; the intact description and the complete formal course learning outcomes are reproduced here.

Course learning outcomes
1. Evaluate, prepare, and analyze data using typical data science methods.
2. Design coding solutions using the Python language and relevant libraries.
3. Implement selected machine learning methods.
4. Recognize ethical issues and observe professional guidelines regarding the use of machine learning and applied artificial intelligence in data science.

The fundamental concepts of probability are essential to solve the data science problem. This course will introduce a broad range of probability theory and mathematical statistics that emphasizes the probabilistic foundations required to understand probability models and statistical. The topics covered include fundamentals of probability theory, statistical inference and graph data structure, including probabilistic models, random variables, useful distributions, expectations, the law of large numbers, the central limit theorem, point and confidence interval estimation, hypothesis tests, basic data structure and algorithms for graphs. By the end of the course the students will be expected to demonstrate the ability to apply fundamental concepts in exploratory data analysis, hypotheses tests, and be able to model problems in data science using graphs. Also, students will be able to use calculators and tables to perform simple statistical analyses for small samples and use popular python statistics packages to perform simple and sophisticated analyses for large samples.

Course learning outcomes
1. Apply fundamental concepts in exploratory data analysis, including random variable, probability of an event, additive rules, and conditional probability.
2. Understand several well-known distributions, including Binomial, Geometrical, Negative Binomial, Normal, and Exponential Distribution.
3. Derive the probability density function of transformations of random variables and use these techniques to generate data from various distributions.
4. Analyze hypotheses tests of means, proportions, and variances using both one- and two-sample data sets. Understanding of data science.

This course introduces fundamental concepts, design principles, and implementation techniques of database management systems. It covers data modeling, schema design, query languages, and integrity constraints, along with core system components such as storage, indexing, and query processing. The course emphasizes both theoretical foundations and practical considerations, including performance analysis and trade-offs in database system design. Students will explore how database systems represent, process, and optimize queries, and how different design choices impact efficiency and scalability. Hands-on components may include the use of existing database systems and/or implementation of selected database functionalities. The specific topics and tools may vary to reflect current practices and advances in the field.

Course learning outcomes
1. Use relational algebra and relational calculus, to express database queries.
2. Use query languages for database management systems.
3. Implement database tables, using functional dependencies and normal forms.
4. Explain core database system components, including storage, indexing, and query processing.
5. Analyze query execution strategies and query plans using appropriate algorithms and techniques.
6. Evaluate and analyze query performance using appropriate estimation methods and optimization techniques.
7. Compare alternative design and implementation choices in database systems and assess their trade-offs.
8. Compare different database systems and choose the suitable one for the given specifications.

This course is a one-semester course intended for graduate students in Data Science graduate program. In this course, students will learn the concepts of process and project management, professional practices, software lifecycle, process models, requirements engineering, and design. Students will be taught theoretical concepts, applications, tools, processes, and practical insights to help them understand how to successfully manage projects in a wide variety of industries including software development and information technology. Students will be exposed to study aids, real-time projects, and tools to provide plenty of hands-on practice.

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

The course will start with setting the context by exploring the relationships between data mining, artificial intelligence, machine learning, deep learning, and statistics fields. It will then introduce knowledge representation, input preparation (preprocessing), fundamental data mining techniques (like probabilistic modeling, linear modeling, association mining, etc.), minimum description length (MDL) principle, validation techniques, decision trees, association rules, instance-based learning models, data transformations, data projections, Bayesian methods, and ensemble learning techniques (bagging, boosting, stacking, etc.) within the supervised, unsupervised and semi-supervised learning frameworks. Data visualization and warehousing methods along with examples from various application areas will also be visited to illustrate the use of data mining in decision support systems and processes.

Course learning outcomes
1. Understand a large set of supervised and unsupervised data mining algorithms along with their advantages and limitations.
2. Select an appropriate algorithm for a given problem and to quantitatively evaluate the fitness of a model generated by the selected algorithm.
3. Develop practical and trustable algorithmic solutions to be used by decision makers.
4. Be ready and well-prepared to participate, work in and/or scientifically contribute to related organizations and industries.

Recent advances in technology have led to orders-of-magnitude increases in the amount of data that is being produced and archived. This phenomenon has been generically referred to as Big Data. Examples of such data include internet traffic and logs, social media, large scale scientific projects, and health records. The process of deriving actionable insights from these large volume and heterogeneous data sets is referred to as Big Data Analytics, and poses new challenges in all aspects of computing. This course will review essential statistical and machine learning techniques for data processing and examine their scalability. Students will gain an understanding of current best practices in Big Data processing and complete assignments and projects with a variety of relevant software tools.

Course learning outcomes
1. Have the required mathematical basis to study big data analytics.
2. Derive a whole range of covered algorithms from first principles.
3. Understand their theoretical basis and assumptions these may have.
4. Implement a range of 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. Understand the bias-variance trade-off (i.e. overfitting/ underfitting).

This course aims to provide the students with a set of principles and skills that will be of paramount importance for conducting their research and disseminating the corresponding results. The course provides an overview of the scientific method and its application, project planning, working in teams, interaction with sources, and assessment of research results. Students are also taught the fundamental principles necessary for writing technical reports, scientific papers, and research proposals, and for preparing and delivering presentations: the focus of this part is on the connection between the employed research methods and the way in which the results are presented, rather than on the writing/presentation style in itself. Finally, students are exposed to a general introduction on moral reasoning, with subsequent focus on ethics in professional practice and research activity. The latter topic will focus on data management and intellectual property rights, and on the description of phenomena such as plagiarism and data fabrication.

Course learning outcomes
1. Asses a research plan and its rationale, including application of the scientific method, interaction with sources, and critical evaluation of research results.
2. Discuss the principles behind the production of reports, papers, proposals, and scientific presentations.
3. Arguing on ethical principles and their application to professional practice and research.

With the rapid advance of information technologies, data is emerging as the world's most important resource. Data-driven innovation offers a huge potential for economic, social and technological progress. In this course, students will learn and discuss concepts such as data-driven research and development, transforming traditional goods and services to data-intensive products, optimizing processes, logistics and delivery using data-driven techniques, and personalized marketing using data analytics, social networking and cloud computing. The course will also discuss how organizational evolution and optimization can be facilitated using data-driven techniques as an accelerator. The students will engage in a semester-long team-based project to create a data-driven innovation project proposal for a governmental or private organization.

Course learning outcomes
1. Explain the principles of data driven concepts and data management and engineering, and apply these principles when engineering data.
2. Analyze large amounts of data using a variety of data engineering tools.
3. Correctly interpret the results derived from these data-driven tools.

Students are led through the processes required to plan and initiate an independent research project that will eventually lead to a thesis. Early on, students are required to finalize their topic and secure the participation of a thesis advisor. They must then develop a research proposal based on a literature analysis of the chosen topic, defend the proposal, and then begin the execution of the project.

Course learning outcomes
1. Identify relevant authoritative sources, conduct a literature search, and produce a detailed bibliography.
2. Define the scope of a research project to replicate published work and/or extend current knowledge in terms of achievable aims.
3. Develop a methodology to conduct and execute the project, including outlining a timeline with milestones, cognizant of resource availability.
4. Prepare and defend a thesis proposal.

The student will conduct independent work under the direction of a supervisor on a research project in the student's designated area of inquiry. This work will culminate in a thesis document that the student must successfully defend. The project itself should demonstrate student mastery of the some or all of the stages of the Data Science life cycle, such as data acquisition, storage, preparation / cleaning, analytics & data mining, modeling & machine learning, and visualization.

Course learning outcomes
1. Conduct independent work on the topic as designated in the Thesis Proposal.
2. Manage task completion in accordance with the established project milestones, and revise the project schedule as needed using critical-path management techniques.
3. Write, using an iterative process, the thesis document in consultation with the thesis advisor.
4. Present the results of the independent work to a technical audience.

The course covers theoretical foundations and applications of machine learning models. Topics include supervised methods for regression and classification (linear models, trees, neural networks, ensemble methods, instance-based methods), Bayesian parametric learning, density estimation and clustering, sequential models. Programming projects covering a variety of real-world applications are assigned.

Course learning outcomes
1. Derive mathematical expressions for a few simple algorithms like ordinary least squares regression, ridge regression, k-means and PCA.
2. Implement clustering, regression and classification algorithms using modern programming languages.
3. Apply gradient descent to solve simple convex optimization problems, and stochastic gradient descent to solve nonconvex optimization problems.
4. Assess whether the solution to a real-world machine learning problem might involve one or more of clustering, regression or classification.
5. Prepare reports and presentations with reproducible code.

Information retrieval (IR) is a discipline concerned with processing, storage, and search of information in large collections of unstructured documents. With development of the internet IR techniques have become a key enabler for accessing the vast amount of online information, with strong social and economic impact. This course is intended for students interested in theoretical foundations of IR and practical experience in building IR systems. During this course, we will discuss a wide variety of IR tasks, approaches, and tools. The course is accompanied by a set of practical assignments that reflect the main steps of the IR pipeline: document processing, indexing, search, and evaluation of results. In addition, we will discuss key research papers in the field of IR in a series of seminars.

Course learning outcomes
1. Demonstrate knowledge related to the main IR approaches and methods.
2. Design and conduct IR experiments, analyze and evaluate obtained results.
3. Implement main steps of IR pipeline: document processing, index construction, query processing, system evaluation.

This course is a one-semester course intended for Masters students in Computer Science and Data Science graduate programs. 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.

This course is a one-semester course intended for graduate students in Computer Science and Data Science graduate programs. It introduces the students to the concepts and existing models and techniques 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; regularization; convolutional neural networks; deep recurrent networks and sequence learning; generative adversarial networks; autoencoders; advanced deep learning topics; applications. The students will learn how to design a deep learning architecture. The course presents several common deep learning techniques and exposes the students in learning how to use deep learning in a very efficient manner. Students will gain experience through assignments and projects, 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 specified tasks, using proper hyper parameters and optimization techniques.
2. Understand and apply concepts related to deep learning, such as logistics regression; gradient descent; regularization; Convolutional Neural Networks; Deep Recurrent Networks; Transformers; Generative Adversarial Networks; Autoencoders.
3. Critically determine how to select a deep learning approach for a domain and task.
4. Gain knowledge about state-of-the-art solutions using deep learning.
5. Understand the strengths and weaknesses of various deep learning approaches.

This is a one-semester course intended for master’s students in Computer Science and Data Science graduate programs. This course explores the interdisciplinary field of intelligent systems, focusing on the design, implementation, and application of systems that demonstrate human-like intelligence. The course covers foundational theories, algorithms, and methodologies used in developing intelligent systems, along with practical applications across various domains. Through a combination of lectures, discussions, case studies, and hands-on projects, students will gain a comprehensive understanding of intelligent systems and the skills needed to design and deploy them effectively.

Course learning outcomes
1. Summarize the fundamental concepts and theories underlying intelligent systems.
2. Explore various approaches and algorithms used in artificial intelligence and machine learning.
3. Design intelligent programs capable of solving subtypes of the most typical of artificial intelligence.
4. Analyze and evaluate the performance of intelligent systems in different application domains.
5. Critically assess current research trends and emerging technologies in the field of intelligent systems.

This course covers the latest advances in probabilistic, statistical and network-based computational models for real world problems such as computer vision and decision making. Topical coverage will begin at hidden Markov Models, through Markov networks, up to conditional random fields. Additionally, we will never cover novel advances in artificial neural networks such as convolutional neural networks ,deep learning models and recurrent neural networks.

Course learning outcomes
1. Utilize common probabilistic graphical models and analyze real-world problems, data.
2. Utilize efficiently the causal reasoning methods available to find problems in statistical reasoning.
3. Construct models from data, verify them and use them on test cases or other real-world situations.

This course is a one-semester course intended for M.S. students in Computer Science and Data Science graduate programs. It enhances the student’s knowledge in computer vision concepts and approaches such as image formation, camera imaging geometry, feature detection and matching, stereo, image classification, scene understanding, and deep learning. In class, we will develop the intuitions and mathematics of the vision approaches. We will expose students to a number of real-world applications that are important to our daily lives. To further differentiate between theory and practice, we will guide students through a series of research projects such that they will have chance to implement cutting-edge computer vision algorithms. Students will also gain experience through research papers, with an emphasis on proper computer vision practices with appropriate applications.

Course learning outcomes
1. Understand and explain concepts related to computer vision such as image formation, feature detection and matching, stereo, image classification, scene understanding, and deep learning.
2. Recognize and describe both the theoretical and practical aspects of computing with images.
3. Successfully implement several interesting and cutting-edge vision algorithms which are widely used in real-world applications such as image retrieval, image captioning, face recognition, scene categorization, and activity recognition.
4. Understand the strengths and weaknesses of various computer vision approaches and apply them to new scenarios.
5. Read and understand scholarly articles in computer vision and come up with new ideas to solve the same problem, which may lead students to write their research paper in computer vision.

An accelerated course on modern software design and implementation, which includes modules on abstraction and encapsulation techniques, component-based design, advanced data structures, algorithms, and performance.

Course learning outcomes
1. Explain why concepts such as abstraction and encapsulation are important in modern software design, and demonstrate with real examples.
2. Explain and show how component-based design and object-oriented approaches to software development can be used to enforce good design principles.
3. Perform asymptotic analysis and amortization to demonstrate the efficiency of an algorithm or data structure.
4. Perform rigorous functional and performance evaluations through testing.
5. Describe the key issues relating to the design and implementation of a new programming language.
6. Implement a simple functional or imperative programming language from a language specification.

Students will learn a number of important topics in the field of computational biomedicine. They will study various topics within the biomedical domain, including primers of anatomy, physiology as well as the basis of various biomedical imaging techniques. Signal processing tools, such as filter theory, artifact rejection, as well as PCA and CSP will be covered. Additionally, we will cover how uni-as well as multi-variate features can be employed for decoding. Practical computing sessions will be carried out and students will also perform a project, where all research-related steps will be covered.

Course learning outcomes
1. Know some basic anatomical and physiological concepts.
2. Understand the basis of various biomedical imaging techniques, such as EEG, NIRS, fMRI, ECG, EMG, among others.
3. Understand which different imaging correlates can be measured and analyzed by various imaging modalities.
4. Understand and implement a range of data analytical techniques, that are common to biomedical related data analysis in particular, but also for computational approached in biomedicine in general.
5. Design and conduct a small biomedical study, i.e. experimental design, implementation, conduction, data analysis, report writing.

This course aims at enabling Master’s students to include cognitive aspects in the modeling of interactive systems in order to capture human errors that would be overlooked when verifying the system in isolation or in an ad hoc interaction context. The course starts with an introduction to cognitive science and a presentation of the human capabilities and limitations and how they affect the interaction process. It then illustrates the use of traditional cognitive models as well as new research approaches. Finally, cognitive modelling is integrated in a software engineering context in order to support the verification of interactive systems. Students’ assessment includes practical modeling exercises, a report and presentation on classical and recent research works, and examinations.

Course learning outcomes
1. Articulate the interdisciplinary aspects of HCI.
2. Link a user’s cognitive capabilities and limitation to the user’s task, determine the critical aspects of the task and assess their complexity.
3. Assess the impact of technology usage on the original user’s task.
4. Predict the right level of abstraction at which to model a given interactive system.
5. Model systems and analyze system properties through the use of specialized tools.
6. Abstract, model and analyze real-world interactive systems in the context of a practical case study.

This graduate course will examine the fundamentals of software testing, development processes, test driven development and related techniques. In particular, the important phases of unit testing, integration testing, system testing, and acceptance testing will be reviewed with an emphasis on their significance within the larger software development process. 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.
5. Critically compare different formal approaches, and be able to choose the most appropriate one for a given situation.

Financial practices have substantially benefited from the domains of computer science (CS) and artificial intelligence (AI), to the point that most trading in major financial markets are now governed by algorithms. This course aims to provide the CS students with the necessary background and guidance in order to have them practically apply their CS and AI knowledge in financial domains. The course consists of 4 parts: In the first part of the course, all necessary financial background will be provided. In the second part, tangible mechanics of the domain (like the Financial Information Exchange, FIX, protocol, and order book dynamics) will be covered. In the third part, algorithmic side of the financial transactions will be elaborated on. And in the last part, quantitative strategies (like portfolio construction, high-frequency trading, news handling, etc.) which make heavy use of statistics, AI, data mining, time series analysis, and performance evaluations will be covered.

Course learning outcomes
1. Become comfortable and fluent with the terminology and practices of the financial applications of computer science and artificial intelligence.
2. Design and develop both client and server sides of trading applications using native Financial Information Exchange protocol.
3. Understand the strengths and weaknesses of major trading algorithms, and to assess and compare their performances.
4. Develop quantitative financial solutions which make use of statistics, artificial intelligence and computer science practices.
5. Be ready and well-prepared to participate, work in and/or scientifically contribute to financial organizations and industries.

The primary aim of this Master's-level course is to help students gain a solid understanding of Software Defined Networks. The course will focus on the decoupling of the control plane and data plane with high level programming abstractions and orchestration. Moreover, it will look at how the network operators can become more flexible in their management of complex network functions, including routing, traffic engineering, security, and resource control. In addition, students will critically evaluate current research trends in Software Defined Networks. Finally, it will provide hands-on experience and practice to students, preparing them for real-world Software Defined Network implementations using Mininet, Mininet_WiFi, and/or Containernet.

Course learning outcomes
1. Describe the key benefits of the separation of the control and data planes in Software Defined Networks (SDNs).
2. Explore an SDN data plane, control plane, and application plane.
3. Configure an SDN-friendly network emulator.
4. Analyze and evaluate the pros and cons of applying SDNs in WANs.
5. Critically assess current research trends and emerging technologies in the field of SDNs.
6. Apply and evaluate security challenges in the SDN paradigm.

Computer networks are the core of modern communication. The activities of modern communications mostly run over Internet Protocol and are computer-controlled. Furthermore, with the usage, and rapid developments of wireless technologies such as cellular networks (including 3G, 4G, 5G, wireless LAN) and infrastructure-less technologies (Ad-Hoc Networks, WSNs), wireless communication systems have also become very important for modern communication. This course covers the distinct concepts and protocols in computer/communication network architectures. Addressing schemes, wireless variants of well-known routing and transport layer protocols, mobile IP and network design issues are considered.

Course learning outcomes
1. Review the distinct concepts and protocols of the wired/wireless network architectures and reflect on how they all fit together.
2. Assess the ways in which the technology of computer networks and wireless communication systems affect the suitability for particular applications.
3. Infer the implications of the specific modes of operation.
4. Test, simulate, and solve typical systems and their abstractions including analytical models for the quality of service analysis.
5. Review and assess security measures and countermeasures.
6. Adapt routing and IP addressing strategies in the design of computer networks to mobile systems.

The aims of the course are to study the fundamental characteristics of distributed systems, including their models and architectures; the implications for software design; some of the techniques that have been used to build them; and the resulting details of good distributed algorithms and applications. It covers abstractions and implementation techniques for the design of distributed systems. Topics include: server design, network programming, storage systems, security, and fault tolerance.

Course learning outcomes
1. Analyze and create formal models of distributed systems.
2. Specify behaviors of distributed systems using temporal logic.
3. Explain key correctness properties of distributed systems, and utilize common strategies for formally demonstrating them.
4. Explain and demonstrate real-world distributed systems problems and their solutions.
5. Utilize software tools to create, simulate, and verify models of distributed and concurrent systems.

The roots of computer science can be traced to modeling things such as trajectories of artillery shells and cryptographic protocols, both of which pushed the development of early computing systems in the early and mid-1940’s. A principal approach to modeling and simulation is abstraction, so that real world systems can be effectively simulated on a machine. In this course, we will examine modeling techniques such as Monte Carlo methods, stochastic processes, queuing theory, and Markov chains. Assessment and evaluation of approaches will also be covered. Important application areas that may be investigated include computer systems and networks, diagnostics, economics and finance, and urban planning.

Course learning outcomes
1. Acquire skills in handling situations involving more than one random variable and functions of random variables.
2. Apply basic probability techniques and models to analyze the performance of computer systems, and, in particular, of networks and queues.
3. Have a well – founded knowledge of standard distributions which can describe real life phenomena.
4. Understand and characterize phenomena which evolve with respect to time in a probabilistic manner.
5. Expose the basic characteristic features of a queuing system and acquire skills in analyzing queuing models.
6. Use discrete time Markov chains to model computer systems.
7. Analyze a network of queues with Poisson external arrivals, exponential service requirements and independent routing.

The course introduces fundamental hardware and software concepts of computer systems as core elements of modern intelligent systems. Topics covered include basic microprocessor architectures, memory allocation, concurrency principles (multitasking, process synchronization), real-time systems, embedded and mechatronic system design. Course project assignments are related to the theoretical material covered in lectures with focus on developing practical skills of embedded system and GUI design using C/C++ programming languages in an embedded Linux operating system.

Course learning outcomes
1. Apply principles of embedded computing, hardware interfacing, and concurrent programming in computer-controlled technical systems.
2. Develop sensor and actuator interfaces on an embedded computing platform using appropriate communication, filtering, control and programming techniques.
3. Integrate sensing, computation, actuation, and control into a functional technical system proto type.
4. Communicate design concepts, technical decisions, experimental results, and system performance effectively in written reports and demonstrations.
5. Work effectively in teams to plan, develop, and deliver a complete engineering project.

This course focuses on the use of formal methods in the modelling, specification and analysis of systems. Various kinds of systems from a number of application domains, such as human-computer interaction, software systems, distributed systems, systems biology and ecology, will be considered. Students will learn how to utilize formal notations and automated tools for simulation and analysis.

Course learning outcomes
1. Articulate the utility of formal methods, and how they relate to and complement other system validation techniques.
2. Apply formal modeling techniques to practical case studies.
3. Formally define system properties using appropriate mathematical notations.
4. Carry out proofs by hand and with the support of automated tools such as model checkers.
5. Critically compare different formal approaches, and be able to choose the most appropriate one for a given situation.

Information Theory is the mathematical theory of communication, and establishes fundamental limits on the capabilities of telecommunications systems. This course presents the foundations of Information Theory including: entropy, channel capacity, source coding, data compression, and rate-distortion theory. The course also reviews relevant topics from signals and systems theory.

Course learning outcomes
1. Understand the fundamental limits of communication in the presence of noise.
2. Quantify the information requirements and overhead of a given problem.
3. Understand and be able to apply different coding techniques.
4. Understand the importance of information theory in the telecommunications industry.

This course covers classical and advanced topics in algorithms. The topics include worst case and expected running time analysis, algorithm design using divide-and-conquer technique, deterministic and randomized sorting algorithms, use of indicator random variables, deterministic and randomized selection algorithms. Then we will talk about data structures including hash tables, universal hashes, bloom filters, binary search trees, priority queues and disjoint sets. Next, we will cover dynamic programming, greedy algorithms and matroids. In the last part of this course, we will talk about graphs, basic graph algorithms, shortest paths, flow networks, max-flow/min-cut and maximum matching. If time permits, we will also cover NP-hardness and approximation algorithms. For each topic, we will also talk about the best known upper and lower bounds in the literature; and possible future research directions and open questions.

Course learning outcomes
1. Understand Big-Oh notation to analyze and compare algorithms in terms of their worst case running time.
2. Analyze randomized algorithms in terms of their expected running time performance.
3. Use fundamental algorithm design techniques including divide-and-conquer, dynamic programming and greedy to solve problems.
4. Explain and use main data structures including priority queues, hash tables with different collision resolution schemes, disjoint sets to obtain efficient implementations of algorithms.
5. Explain comparison-based sorting algorithms along with lower bounds on sorting, primary search and order statistics algorithms.
6. Explain what graphs are, different data structures for graph storage and basic graph traversal algorithms.
7. Devise algorithms to solve path and connectivity problems on graphs, using methods learnt thus far, including shortest paths and network flows.
8. Explain what Non-determinism is and the concept of NP-hardness and use reductions to prove NP-hardness.

This course introduces the students to best-practice pedagogical methods and innovations in teaching and learning, under the mentorship of senior faculty. The students will conduct classroom and laboratory observations of prominent instructors, using a variety of teaching and learning styles. Later, they will apply educational, instructional, and assessment methodologies in recitation sections of their respective disciplines under the supervision of an experienced faculty member. They will summarize their observations and experiences in weekly activity logs and a final summative report.

Course learning outcomes
1. Recognize high-level program and curricular structure & purpose.
2. Identify and describe a variety of pedagogical methods.
3. Specify specific learning objectives and outcomes for a given topic.
4. Utilize Bloom’s Taxonomy in the articulation of learning outcomes.
5. Select appropriate assessment methodologies suitable to determine student achievement of stated learning outcomes.
6. Identify indicators of potential academic misconduct, and appropriate responses.
7. Conduct the reflective exercises necessary to implement ongoing quality enhancement processes.

Students will apply educational and instructional methodologies in laboratory and practical sessions of their respective disciplines under the supervision of an experienced faculty member. They will summarize their observations and experience in a final report.

Course learning outcomes
1. Identify pedagogical methods for laboratory sessions.
2. Select appropriate assessment methodologies for evaluate the level of achievement of learning outcomes of the specific lab activity.
3. Conduct reflective exercises to assess the effectiveness of the laboratory learning experience, and implement modifications aimed at quality enhancement.