This course provides a comprehensive overview of different concepts and methods related to deep learning. Students will first learn the foundations of deep learning, after which they will be introduced to a series of deep models: convolutional neural networks, autoencoders, recurrent neural network, and deep generative models. Students will work on case studies of deep learning in different fields such as computer vision, medical imaging, natural language processing, etc.

The course provides a comprehensive introduction to the concepts, principles and methods of geometry-aware computer vision which helps in describing the shape and structure of the world. In particular, the objective of the course is to introduce the formal tools and techniques that are necessary for estimating depth, motion, disparity, volume, pose and shapes in 3D scenes.

This course provides a comprehensive overview of different concepts and methods related to visual object recognition and detection. In particular, the students will learn a large family of successful and recent state-of-the-art architectures of deep neural networks to solve the tasks of visual recognition, detection and tracking.

This course provides a comprehensive introduction to Digital Twins. Students will learn about digital twin technology, its common applications, and benefits, how to create a digital twin for predictive analytics using sensory data fusion, and primary predictive modeling methods, and how to implement and interacts with a digital twin using different platforms.

This course is an introductory course on data mining, which is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.

This course is an introductory course on big data processing, which is the process of analyzing and utilizing big data. The course involves methods at the intersection of parallel computing, machine learning, statistics, database systems, etc.

This course provides a graduate-level introduction to the principles and methods of medical imaging, with thorough grounding in the physics of the imaging problems. This course covers the fundamentals of X-ray, CT, MRI, ultrasound, and PET imaging. In addition, the course provides an overview of 3D geometry of medical images and a few classical problems in medical images analysis including classification, segmentation, registration, quantification, reconstruction and radiomics.

This course provides a comprehensive introduction to machine learning. It builds upon fundamental concepts in mathematics, specifically probability and statistics, linear algebra, and calculus. Students will learn about supervised and unsupervised learning, various learning algorithms, and basics of learning theory, graphical models, and reinforcement learning.

Probabilistic and statistical inference is the process of drawing useful conclusions about data populations or scientific truths from uncertain and noisy data. This course will cover different modes of performing inference including statistical modelling, data-oriented strategies, and explicit use of design and randomization in analyses. Furthermore, it will provide an in-depth treatment of the broad theories (frequentists, Bayesian, likelihood) and numerous practical complexities (missing data, observed and unobserved confounding, biases) for performing inference. This course presents the fundamentals of statistical and probabilistic inference and shows how these fundamental concepts are applied in practice.

This course comprehensively introduces using AI/ML in smart city services and applications. The course will start by reviewing basic concepts. Students will learn how to apply AI/ML to develop, design, and improve smart city services. They will be able to demonstrate an understanding of the smart city concept, applications, requirements, and system design. They will develop capabilities of integrating emerging technologies in smart city components and be able to implement them. In addition, they will gain knowledge in applying security, data analytics, Internet of Things (IoT), communications, and networking and work on case studies solutions for smart city infrastructures.

This course provides students with a comprehensive introduction to various trust-related issues in artificial intelligence and machine learning applications. Students will learn about attacks against computer systems that use machine learning and defense mechanisms to mitigate such attacks.

This course provides a comprehensive introduction to using AI/ML in Internet of Things (IoT) smart systems, services and applications. The course will start by reviewing advanced concepts. Students will learn how to apply AI/ML to develop, design and improve IoT systems and services. They will be able to demonstrate an understanding of IoT concepts, applications, requirements and system design. They will develop capabilities of integrating emerging technologies in smart IoT components and be able to implement them. In addition, they will gain knowledge and skills in applying security, data analytics, AI models, communications and networking and work on case studies solutions for IoT infrastructures.

This course provides a comprehensive introduction to natural language processing (NLP). It builds upon fundamental concepts in mathematics, specifically probability and statistics, linear algebra, and calculus, and assumes familiarity with programming.

This course provides a comprehensive introduction to natural language processing (NLP). It builds upon fundamental concepts in NLP and assumes familiarization with mathematical concepts and programming.

This course provides a comprehensive introduction to speech processing. It builds upon fundamental concepts in speech processing and assumes familiarization with mathematical and signal processing concepts.

The course covers the mathematical foundation of robotic systems and introduces students to the fundamental concepts of ROS (Robot Operating System) as one of the most popular and reliable platforms to program modern robots. It also highlights techniques to formally model and study robot kinematics, dynamics, perception, motion control, navigation, and path planning. Students will also learn the interface of different types of sensors, read and analyze their data, and apply it in various robotic applications.

Master’s thesis research exposes students to an unsolved research problem, where they are required to propose new solutions and contribute towards the body of knowledge. Students pursue an independent research study, under the guidance of a supervisory panel, for a period of one year. Master’s thesis research helps train graduates to pursue more advanced research in their Ph.D. degree. Further, it enables graduates to independently pursue an industrial project involving a research component.

This course focuses on teaching students how to develop innovative research-based approaches that can be implemented in an organization. It covers various research designs and methods, including scientific methods, ethical issues in research, measurement, experimental research, survey research, qualitative research, and mixed methods research. Students will gain knowledge in selecting, evaluating, and collecting data to address specific research questions. Additionally, they will learn design thinking skills to connect their research-based topic to practicality. After completing the course, students will have the skills to develop a full research topic that can be innovative, entrepreneurial, and sustainable and can be applied in any organization related to the topic of research.

M.Sc. Internship (up to six weeks)