CSI 4106. Introduction to Articial Intelligence – Fall 2024

Author

Marcel Turcotte

Warning

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

Description

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

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. 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)

Grading

The final course grade will be calculated as follows:

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

Material and resources

Monographs

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

Academic integrity

Academic fraud is an act by a student that may result in a false evaluation (including papers, tests, examinations, etc.). It is not tolerated by the University. Any person found guilty of academic fraud will be subject to sanctions.

Here are some examples of academic fraud:

  • Plagiarism or cheating of any kind;
  • Present research data that has been falsified;
  • Submit a work for which you are not the author, in whole or part;
  • Submit the same piece of work for more than one course without the written consent of the professors concerned.
  • Please consult this webpage: it contains regulations and tools to help you avoid plagiarism.

An individual who commits or attempts to commit academic fraud, or who is an accomplice, will be penalized. Here are some examples of possible sanctions:

  • 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

Student services

Academic writing help

At the Academic Writing Help Centre you will learn how to identify, correct and ultimately avoid errors in your writing and become an autonomous writer. In working with our Writing Advisors, you will be able to acquire the abilities, strategies and writing tools that will enable you to:

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

Further information is available here:

Career services

Career Services offers various services and a career development program to enable you to recognize and enhance the employability skills you need in today’s world of work.

Counselling service

Counselling and therapy is a confidential service for students who are facing life challenges. It is a safe space to explore new perspectives and build resilience.

Access Service

The Access Service acts as an intermediary between students, their faculty and other University offices to ensure that the special needs of these students are addressed and that the best possible learning conditions are being offered.

Note that the University of Ottawa is affiliated with AERO and ACE services for the adaptation of accessible academic materials for students with perceptual disabilities. If you have any questions, please contact the Accessibility Librarian or the Access services for textbooks.

Support and prevention of sexual violence

The University of Ottawa will not tolerate any act of sexual violence. This includes acts such as rape and sexual harassment, as well as misconduct that take place without consent, which includes cyberbullying. The University, as well as various employees and student groups, offers a variety of services and resources to ensure that all uOttawa community members have access to confidential support and information, and to procedures for reporting an incident or filing a complaint. For more information, please visit www.uOttawa.ca/sexual-violence-support-and-prevention.