Course Logistics

Lecture: Mon/Wed 02:00 PM - 03:30 PM
Location: ECJ 1.214
Unique Number: 55340
Canvas: Class site
Instructional Mode: Face-to-face
Syllabus: Available through Canvas

Course Materials

Required: Course manuscript
Required: Reading assignments provided on Canvas.
Recommended: Artificial Intelligence: A Modern Approach, 4th US ed.
Supplementary: Understanding Deep Learning for the final module.

Prerequisites

Prerequisite: The following coursework with a grade of at least C- in each: Computer Science 429 or 429H; and Mathematics 362K or Statistics and Data Sciences 321. Only one of the following may be counted: Computer Science 343; 343H; 378H (Topic: Artificial Intelligence: Honors).

Course Overview

CS 343H introduces the foundations and applications of artificial intelligence, including search, probabilistic reasoning, intelligent agents, machine learning, planning, natural language processing, robotics, computer vision, and philosophical foundations.

The course is organized around five modules:

1. Background review
2. Deterministic search: informed and uninformed search, constraint satisfaction problems, and adversarial search
3. Probabilistic search: Monte Carlo search, Markov decision processes, and reinforcement learning
4. Probabilistic modeling and graphical models
5. Deep learning: convolutional neural networks, transformers, and applications in image and language processing

See Canvas for the detailed timetable.

Learning Outcomes

At the conclusion of this course, students will be able to:

• Design and analyze autonomous agents for known, unknown, adversarial and stochastic environmental settings
• Learn various representations used for solving AI problems
• Gain hands-on experience through course projects
• Learn AI applications in natural language processing, computer vision and robotics