CSI 4106. Introduction to Articial Intelligence – Fall 2024

Author

Marcel Turcotte

Published

September 18, 2024

Course info

Day Time Location
Lecture 1 Monday 13:00-14:20 FSS 2005
Lecture 2 Wednesday 11:30-12:50 FSS 2005
Office hours Wednesday 15:00-16:20 STE 5106

Description (official)

The roots and scope of Artificial Intelligence. Knowledge and knowledge representation. Search, informed search, adversarial search. Deduction and reasoning. Uncertainty in Artificial Intelligence. Introduction to Natural Language Processing. Elements of planning. Basics of Machine Learning.

Approach

Machine learning first. Unlike other artificial intelligence courses, this one is structured so that deep learning is presented as early as possible. This serves two purposes. First, this is because deep learning is such a dominant technology now that it would be difficult to grab students attention without studying this subject. Second, deep learning serves as a framework through which we can introduce and define key topics in artificial intelligence. In some cases, we will see how deep learning has displace other technologies, or replace parts of it. Whereas in other cases, this will be an opportunity to discuss the limitations of deep learning and see how previous technologies had been developed for solving specific problems. Finally, it is important to note that learning itself represents one of the earliest and most extensively understood milestones in the evolution of intelligence.

Learning outcomes

Upon completion of the course, you will be able to:

  • Explain the fundamental concepts and historical development of Artificial Intelligence (AI)
  • Apply problem-solving strategies using AI techniques
  • Critically analyze and compare different AI approaches
  • Demonstrate independent learning and exploration

Outline

Given the widespread influence of deep learning in current Artificial Intelligence advancements, my aim is to incorporate it into the course curriculum at an early stage. Establishing this groundwork will facilitate our understanding of its significance, particularly as we delve into subjects such as Monte Carlo Tree Search (MCTS) or Reinforcement Learning (RL) later in the course. Below is a preliminary and ambitious course outline.

  1. Machine learning
    1. Introduction
    2. Linear regression and logistic regression
    3. Neural networks
  2. Deep Learning (2)
  3. Solution spaces
    1. Heuristics
    2. Constraint satisfaction/optimization: scheduling, TSP
    3. Case study: knapsack, population-based search
    4. Games and adversarial searches
  4. Reinforcement Learning
  5. Reasoning
    1. Propositional and predicate logic
    2. Logic and uncertainty
    3. Knowledge representation and reasoning (2)
  6. Natural Language Processing (2)
  7. Generative AI (2)
  8. Large Language Models (LLMs)

Grading

The final course grade will be calculated as follows:

Category Percentage
Assignments 40% (4 x 10%)
Quiz 20%
Final examination 40%

Consult the schedule for the dates.

Material and resources

Monographs

I will draw upon insights from the two comprehensive textbooks listed below, as well as relevant scientific publications. All sources of information will be cited. For most people, I expect that my lecture notes will be sufficient.

The Campus Store has ordered a small number of copies of these books, for those interested.

Acknowledgement

I extend my gratitude to Caroline Barrière for granting access to her comprehensive course materials.

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