Provides a solid foundation to concepts of Probability Theory, Random Processes and Statistics required for Engineers designing and using AI. The course starts with axiomatic definition of probability metric and uses this to build the foundation on conditional probability, Baye's Theorem, and definition of probability density and distribution functions of discrete and continuous random variables. This foundation is then used to understand analyses of functions of one or more random variables, define moments and conditional moments of random variables and apply these concepts to parameter estimation and prediction. The class will emphasize the importance and wide applicability of Gaussian random variables. Hypothesis testing and its applicability to Artificial Intelligence will be explored. The class concludes with fundamentals concepts of Random Processes and use of Markov Chains for analyzing state transitions.

Covers the fundamentals of optimization, from formulating a mathematical optimization problem from a problem description, to solving a mathematical optimization problem using numerical algorithms in optimization software, with an emphasis on convex optimization. The main topics include: linear algebra overview; convex sets and convex functions; convex optimization; duality theory and optimality criteria, Karush-Kuhn-Tucker conditions; reinforcement learning; unconstrained optimization algorithms: gradient method, Newton's method, quasi-Newton methods; constrained optimization algorithms: conditional gradient method, gradient projection method, alternating direction method of multipliers, interior point method, primal-dual method; stochastic gradient descent; distributed optimization; global search algorithms. Students will acquire not only theoretical knowledge of optimization, but also hands-on experience with optimization methods and software through assignments and a project.

Provides a comprehensive introduction to Python programming, covering fundamental concepts such as program structure, variables, assignments, and built-in data types, including strings, lists, tuples, and dictionaries. Students explore control flow, functions, modules, and basic I/O and file operations. The course also introduces object-oriented programming, classes, and exception handling. Beyond the basics, students delve into algorithms and data structures, including recursion, searching, graph algorithms, priority queues, search trees, and hash tables. The course explores algorithms used in artificial intelligence and machine learning, such as regression, classification, and clustering. Students gain hands-on experience with essential scientific computing libraries, including NumPy, SciPy, and Matplotlib. By the end of the course, students will have a solid foundation in Python programming and its applications in data structures, algorithms, and AI-driven problem-solving.