Marek Reformat

Professor, Faculty of Engineering - Electrical & Computer Engineering Dept

Continuing Ed Fall 2021 (1766)

EXEN 2451 - Machine Learning Applications

★ 36

This module offers an introduction to a variety of unsupervised and supervised methods of data processing. Learn different architecture configurations for predictive modeling, kernel methods, neural networks, and techniques for evaluation of model performance. You'll bring real-world problems from your own workplace, and use machine learning to solve them. With access to the state-of-the-art resources in the Faculty of Engineering, and leading researchers in the area, your learning will be hands-on and practical with application to industry. Prerequisite: Restricted to students admitted into the Certificate for Artificial Intelligence

LECTURE FA1 (40383)

Fall Term 2021 (1770)

ECE 720 - Advanced Topics in Software Engineering and Intelligent Systems

★ 3 (fi 6)(EITHER, 3-0-0)

LECTURE A12 (58278)
LECTURE A13 (58279)

Winter Term 2022 (1780)

ECE 627 - Intelligent Web

★ 3 (fi 6)(EITHER, 3-0-0)

Representation, processing, and application of knowledge in emerging concepts of Semantic Web: ontology, ontology construction, and ontology integration; propositional, predicate and description logics; rules and reasoning; Semantic Web services; Folksonomy and Social Web; Semantic Web applications.

LECTURE B1 (62160)
2022-01-05 - 2022-04-08
MWF 12:00 - 12:50 (NRE 2-016)


ECE 220 - Programming for Electrical Engineering

★ 3 (fi 8)(EITHER, 3-0-3/2)

Architecture and basic components of computing systems. Programming environment and program development methodology. Basics of programming: from data structures and functions to communication with external devices. Principles of object-oriented programming. Good programming style. Prerequisite: ENCMP 100.

LECTURE B1 (68768)
2022-01-05 - 2022-04-08
MWF 13:00 - 13:50 (T LB-001)

Continuing Ed Spring 2022 (1786)

EXEN 2453 - Reinforcement Learning Applications

★ 36

An introduction to principles of reinforcement learning that include algorithms supporting action decision processes that optimize long-term performance. Topics include: dynamic programming, Q-learning, Monte Carlo reinforcement learning, and efficient algorithms for single- and multi-agent planning. Co-requisite: EXEN 2452

LECTURE SP1 (30001)