CSI 4106. Introduction to Articial Intelligence – Fall 2024


Marcel Turcotte


This website is currently being developed. Specifically, the syllabus is in draft form and will be subject to modifications. Feedback and suggestions are most welcomed.

Course info

Day Time Location
Lecture 1 Monday 13:00-14:20 DMS 1160
Lecture 2 Wednesday 11:30-12:50 DMS 1160
Office hours Wednesday 15:00-16:20 STE 5106


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.

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


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. Naïve Bayes classifier
    3. Neural networks
    4. Linear regression and logistic regression
  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)


The final course grade will be calculated as follows:

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

Material and resources


This course does not require a mandatory textbook. However, I will be drawing inspiration from two textbooks.

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  • Receive an “F” for the work or in the course in question;
  • Imposition of additional requirements (from 3 to 30 credits) to the program of study;
  • Suspension or expulsion from the Faculty.
  • You can refer to the regulations on this web page

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  • Master the written language of your choice
  • Expand your critical thinking abilities
  • Develop your argumentation skills
  • Learn what the expectations are for academic writing

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