Bachelor of Science in Robotics Engineering

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 is a one-semester course intended for mathematics, engineering, and science students. It covers first order differential equations; mathematical models and numerical methods; linear systems and matrices; higher-order linear differential equations; linear systems of differential equations; and Laplace transform methods.

Course learning outcomes
1. Classify differential equations and learn basic concept related to ordinary differential equations.
2. Solve linear and some simple nonlinear ordinary differential equations using different solution techniques.
3. Solve systems of first order linear ordinary differential equations.
4. Describe the general behavior of the solutions to differential equations and systems of first order linear ordinary differential equations.
5. Model simple real-world problems using ordinary differential equations.
6. Employ Laplace transform for linear ordinary initial value problems.

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 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 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.

ROBT 201 is an introduction to engineering mechanics. The first part of the course covers Statics, including equilibrium of a particle and of a rigid body in 2-D and 3-D, force and moment resultants, internal forces and moments, trusses and frames, basics of structural analysis. The second part of the course introduces students to Dynamics, and in particular, to particle kinematics and kinetics in 2-D and 3-D, to rigid body kinematics and kinetics in 3-D, to concepts of work-energy, impulse-momentum, force-acceleration and to translational and rotational motion.

Course learning outcomes
1. Understand concepts of equilibrium, motion, work, energy, momentum and etc.
2. Solve engineering problems related to statics and dynamics of particles and of rigid bodies.
3. Intuitively grasp simple mechanical problems.

To perform transient and sinusoidal steady-state analysis of RL, RC, and RLC circuits, Norton-Thevenin equivalent representation, learn operational amplifiers, and become acquainted with the fundamentals of Laplace transforms as they apply to electric circuit analysis, and Bode plots. Non-linear passive circuit elements (diodes). Brief introduction to bipolar junction transistor and diodes. There are 3-hour lab sessions per week from the 2nd week.

Course learning outcomes
1. Be able to apply Ohm’s Law, Kirchhoff’s Current Law, and Kirchhoff’s Voltage Law, equivalent resistance calculation, voltage divider, current division in analysis of linear circuits consisting of resistors, inductors, and capacitors driven by constant and sinusoidal voltage and current sources.
2. Be able to apply the Node Voltage and Mesh Current circuit analysis techniques.
3. Be able to calculate voltages, currents, and gains of circuits containing operational amplifiers.
4. Be able to calculate the Thevenin or Norton equivalent of a circuit.
5. Demonstrate skills to use and apply of oscilloscopes, signal generators, multi-meters and power supplies, individually or as part of a team.
6. Be able to solve circuit analysis and design problems using Matlab software.

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.

System concept, Linear Time Invariant Systems, Laplace Transform, lumped parameter modeling of mechanical systems, transfer-function approach to modeling dynamic systems, state- space approach to modeling dynamic systems, introduction to modeling electrical systems and electromechanical systems, time- domain analyses of dynamic systems, frequency-domain analyses of dynamic systems. Prerequisites: Calculus II, Physics II w/Lab

Course learning outcomes
1. Understand concepts of Laplace transform, Inverse Laplace Transform, state-space and transfer function methods.
2. Solve engineering problems related to the analysis of dynamics of mechanical and electrical systems.
3. Analyze controllability and observability of linear systems, learn essentials of linearization.

Prerequisites: Electrical and Electronic Circuits I W/Lab (PHYS251/ROBT203) Basic operation of bipolar junction transistors and metal oxide semiconductor transistors. Operational amplifiers: ideal op-amp, inverting and non- inverting configurations, op-amp circuits focusing on filters. Introduction to logic circuits. Introduction to circuit analysis and printed circuit board design. 3 hour lab session.

Course learning outcomes
1. Understand gain, phase shift and Bode plots for electrical circuits and be able to apply Laplace Transform for the solution of electrical circuits.
2. Understand the operating principles of semiconductor diodes and be able to apply diodes in different applications.
3. Understand the operating principles of bipolar junction transistors and field effect transistors.
4. Be able to apply and design circuits with operational amplifiers and power amplifiers using transistors as well as using professional software for circuit simulation and printed circuit board design.

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 focuses on the fundamentals of mechanical design which lays the analytical foundation needed for the design of machine elements. The topics include the fundamentals of mechanical design, materials and processes, solid mechanics, stress, strain and deflections, static and fatigue failure theories and FEA. Laboratory sessions of the course teach students basic skills of computer- aided design (CAD) and exposes them to different manufacturing processes with major emphasis on additive and subtractive manufacturing technologies. SolidWorks CAD software is used to introduce 3D solid modeling, assembling, structural analysis and motion simulation of common machine elements, such as shafts, bearings, gears, springs, screws and fasteners. After mastering basic skills of computer-generated design students will engage in the process of 3D printing with plastic and machining with metal. Machining methods that are explained include drilling, cutting, bending, turning, milling, grinding, and basis of CNC machining.

Course learning outcomes
1. Understand the need, process and professional/safety requirement in mechanical design.
2. Understand the material properties and processes required for designing machine elements.
3. Understand and analyze different types of loading, stress, strains and deflections on machine elements.
4. Understand the static, fatigue and surface failure theories.
5. Design mechanical parts and systems using 3D computer aided design tools with proper documentation and realize these components using rapid prototyping techniques.
6. Apply Finite Element Analysis (FEA) to evaluate the mechanical properties of mechanical parts with different materials.
7. Understand the mechanical elements including; shafts, keys and couples; Bearings; Spur, helical and worm gears; Springs and fasteners.

This course is intended to introduce students to concepts and techniques of classical control and to briefly introduce some concepts of modern control and discrete-time. The main goal is to enable students to analyze, design, and synthesize linear control systems. Students will become familiar with analytical methods and will be exposed extensively to the use of computers for analysis and design of control systems. Prerequisites: System Dynamics and Modeling, Linear Algebra with Applications, Introduction to Differential Equations

Course learning outcomes
1. Analyze the stability and performance properties of single- input-single-output (SISO) linear control system, both using the root locus and the frequency-domain techniques, both analytically and using numerical simulation.
2. Synthesize controllers for single- input-single-output (SISO) linear control system, both using the root locus and the frequency-domain, both analytically and using numerical simulation.
3. Design PID controllers, using any of the previously-mentioned methods, knowing about the practical issues in their implementation (anti wind-up, discretization, etc.).
4. Analyze systems in the state-space framework, and be aware of the main design methods design related to the state-space domain.

This course will introduce students with the theoretical and technological foundations of classical and modern electromechanical systems. Emphasis will be given to electrical drives and machines that are employed in robotics and automation. Students will gain basic knowledge in electromagnetics and magnetic circuits analysis to predict electromagnetic characteristics of electric machines and permanent magnet materials. Using principles of electromechanical energy conversion they will be able to predict forces and torques for different kind of electric machines. For the lab sessions of this class, the students will design and build their own electric servo-drive based on brushless DC motors. By using Matlab software and the real-time capability of Simulink they will implement torque, velocity, and position control.

Course learning outcomes
1. Analyze and solve magnetic circuits, which include soft and hard ferromagnetic materials, coils, and moving mechanical parts (plungers, rotors).
2. Use the basic principles of electromechanical energy conversion to predict forces and torques in rotational and linear electric machines.
3. Learn fundamentals and basic operating characteristics of common electrical machines.
4. Understand, evaluate and compare different motor technologies such as AC synchronous/asynchronous, induction, and DC brushed/ brushless.
5. Design and control a servo drive by using rapid prototyping techniques based on Matlab/ Simulink software.

This course covers classical topics in robotics with particular emphasis on the kinematics, and dynamics of industrial manipulators. Different kinematics architectures are introduced together with classical modeling techniques based on homogeneous transformations. Alternative methods, such as the fixed and Euler angle conventions, quaternions and the vector-angle representation, are explained to represent the orientation of rigid bodies. Analysis of velocities, static forces, and kinematic singular configurations is carried out by means of differential kinematics. The equations of motion of a manipulator are derived using Newton-Euler and Lagrange methods. The analytical closed form solution for the inverse dynamic problem is obtained ready to be used as the core block for model-based control techniques and for simulation purposes.

Course learning outcomes
1. Understand the working principles, features, and usages of industrial manipulators and related safety issues.
2. Formalize and analyze the forward and inverse kinematics of open- chain manipulators.
3. Obtain the differential kinematic model of a manipulator and calculate the correspondent Jacobian Matrix.
4. Formalize the inverse dynamic model using Newton-Euler and Lagrange methods.

This course introduces control, modeling and learning techniques applied to industrial robot- manipulators and mobile robots. Students are expected to learn the principles of robot motion control and trajectory planning methods formalized in the robot joint and task spaces. The theoretical concepts will be tested with the support of different robot modeling and simulation environments, such as MATLAB/Simulink, V-REP and Gazebo using a widely applied in robotics Robot Operating System (ROS) software framework for control and learning algorithm implementation. Modern learning techniques are presented to robot state estimation and sensor data processing. The course is structured around weekly lectures that are complemented by computer based lab sessions

Course learning outcomes
1. Describe the working principles, features, and usages of industrial and mobile robot.
2. Implement Trajectory Planning in the Cartesian and the Joint Space.
3. Develop feedback and model based control systems for robotics.
4. Set up ROS-based control architectures.
5. Exploit the knowledge acquired on artificial neural networks and use different simulation software to design and test motion planning and control systems for robotics endowed with learning capabilities.

This course integrates the design of electric/electronic, mechanical, and embedded computer systems to provide students with a comprehensive understanding of advanced methods for developing robotic and mechatronic systems. It builds on knowledge and practical skills acquired in earlier courses, emphasizing the integration of hardware and software in real engineering applications. Throughout the course, students complete a series of hands-on projects that culminate in the design of a complete robotic/mechatronic system with specified dynamic performance and sensor requirements. Through these activities, the course provides practical experience closely aligned with real-world robotics and mechatronics engineering practice.

Course learning outcomes
1. Apply principles of embedded computing, hardware interfacing, and concurrent programming in robotic and mechatronic 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 robotic or mechatronic prototype.
4. Communicate design concepts, technical decisions, experimental results, and system performance effectively in written reports and demonstrations.
5. Analyze functional, dynamic, sensing, and control requirements of a robotic or mechatronic system.
6. Work effectively in teams to plan, develop, and deliver a complete engineering project.

The course covers both the fundamentals of critical reading, writing and research document analysis as well as composition of scientific papers. Covered topics include reading and analysis of selected journal and conference papers, writing summary from selected sources, understanding of research material, finding topic and adviser and writing a full proposal with literature background for their capstone project.

Course learning outcomes

1. Explain the basic concepts related to research, such as the scientific method, the planning of projects, how to work in teams, interact with sources, and assess research results.
2. Write a survey paper on a topic in their field of interest, comparing and integrating concepts from multiple sources.
3. Write a research proposal for a capstone project.

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.

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.

This capstone project course serves as the culmination of students' academic journey in their major, providing an opportunity to integrate and apply their knowledge and skills within the robotics and mechatronics engineering field. The Fall semester's capstone project course marks the beginning of an intensive, hands-on experience in robotics and mechatronics engineering. Students work in teams on research and development (R&D) solutions that address current needs in the field. This semester focuses on the initial stages of the project lifecycle, including research, design, and preliminary development. Emphasizing students' strategic planning and collaborative teamwork, the course aims to foster leadership and project management skills. By the end of the semester, students are expected to submit a written report and orally present their work, demonstrating their approach to engineering problem- solving and effective communication skills. This foundational work prepares them for the subsequent phase of project implementation and testing in the Spring semester.

Course learning outcomes
1. Identify, formulate, and solve engineering problems related to robotics and mechatronics disciplines.
2. Design robotic/mechatronic systems or components to meet desired needs within realistic constraints.
3. Communicate effectively with a range of audiences.
4. Familiarize with research methods and acquire research skills.
5. Work effectively in teams.

Building on the foundational work of the Fall semester, the Spring graduation project course continues to advance students' proficiency in robotics and mechatronics engineering. In this semester, students will take their R&D projects to the next level by implementing and testing their designs, ensuring they meet the specified requirements. This phase encourages deeper engagement with technical challenges and innovative problem-solving. Students will refine their projects based on feedback and testing outcomes, enhancing their ability to adapt and optimize solutions. The course maintains a strong emphasis on teamwork and leadership, as students manage project milestones and integrate interdisciplinary knowledge. The semester culminates in a comprehensive demonstration of the final project, alongside a detailed report and an enhanced oral presentation that showcases their advanced engineering skills and readiness for professional careers.

Course learning outcomes
1. Apply standard project management tools to ensure timely completion of a project.
2. Verify and validate project objectives based on relevant engineering standards and design criteria.
3. Present project results both orally and in writing.

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.

Deals with switching issues in switch mode power supplies and high power converters and inverters. The switch mode power supply section investigates the operation and design of the standard switching power supply topologies such as the buck, boost, buck-boost, and Cuk switch mode converters. There is a brief introduction to resonant converter technology. The second section of the course considers high power converters and inverters. The terminal characteristics of the power devices used are investigated. Standard inverter topologies introduced, and various PWM algorithms for them are developed. Power quality issues and some solutions are presented. Multilevel converter topologies are introduced.

Course learning outcomes
1. Be able to select a converter for a specific application.
2. Be able to design a converter based on specific application requirements.
3. Be able to select semiconductor devices (different types, voltage and current ratings, switching frequency).
4. Be able to create a controller for the all class covered topologies.
5. Be able to simulate power electronics converters using Matlab/ Simulink (or PSpice) software and implement power converters in practice.

Introduction to automation and plants, programmable logic controllers (PLCs), industrial sensors, industrial communication buses (CAN, ASI, Fieldbus), G-Code for CNC machines, operation with industrial robotic manipulators, pneumatic and hydraulic components.

Course learning outcomes

1. Define industrial automation and the concepts behind it.
2. Identify Programmable logic controllers and know their specific properties.
3. Program a PLC using the provided software and the determined programming language.
4. Explain the function of different industrial sensors and actuators which are used in the industry.
5. Discuss about industrial networking protocols.
6. Use the CODESYS software for simulating PLCs and developing PLC programs accompanied with visualizations.
7. Work with Siemens s7 1200 PLCs and HMI panels using the TIA portal software.
8. Choose a proper standard PLC programming lang.

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.

Robotics Engineering students have an opportunity to complete a credit-bearing internship from the third year onwards. The internship provides a practical problem-solving experience as a bridge between coursework and professional life. A student can take the internship at a company, located either in Kazakhstan or in a foreign country, for a total number of working hours equal to 120 or more. As an alternative, the internship can be taken either in a research laboratory of a foreign university, for total number of hours of research conducted equal to 120 or more. After completion of the internship, the student must submit an internship report summarizing the tasks accomplished and reflecting on the experience.

Course learning outcomes
1. Apply existing engineering knowledge for industrial and/or research problems.
2. Identify when new engineering knowledge is required and integrate it with the existing knowledge to solve practical engineering problems.
3. Understand lifelong learning processes through critical reflection of internship experience.
4. Communicate effectively with interdisciplinary teams.

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.

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.