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Introduction to Artificial Intelligence

Fall 2019



Prof. Gilles Louppe
[email protected]

Logistics

This course is given by:

  • Theory: Gilles Louppe
  • Exercises: Antoine Wehenkel
  • Projects: Samy Aittahar, Pascal Leroy and Florian Merchie

Feel free to contact us at [email protected] for help!

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Outline

  • Lecture 0: Artificial intelligence
  • Lecture 1: Intelligent agents
  • Lecture 2: Solving problems by searching
  • Lecture 3: Constraint satisfaction problems
  • Lecture 4: Adversarial search
  • Lecture 5: Representing uncertain knowledge
  • Lecture 6: Inference in Bayesian networks
  • Lecture 7: Reasoning over time
  • Lecture 8: Making decisions
  • Lecture 9: Learning
  • Lecture 10: Communication
  • Lecture 11: Artificial General Intelligence and beyond

Philosophy

Thorough and detailed

  • Understand the landscape of artificial intelligence.
  • Be able to write from scratch, debug and run (some) AI algorithms.

Well established algorithms and state-of-the-art

  • Well-established algorithms for building intelligent agents.
  • Introduction to materials new from research ($\leq$ 5 years old).
  • Understand some of the open questions and challenges in the field.

Practical

  • Fun and challenging course project.

Lectures

  • Theoretical lectures
  • Exercise sessions

Materials

Slides are available at github.com/glouppe/info8006-introduction-to-ai.

  • In HTML and in PDFs.
  • Posted online the day before the lesson (hopefully).
  • Minor improvements/fixes from previous years.

Some lessons and materials are partially adapted from "Introduction to Artificial Intelligence" (CS188), from UC Berkeley.

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Textbook

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The core content of this course is based on the following textbook:

.italic[Stuart Russel, Peter Norvig. "Artificial Intelligence: A Modern Approach", Third Edition, Global Edition.]

This textbook is strongly recommended, although not required.

Projects

Reading assignment

Read a major scientific paper in Artificial Intelligence. (Paper to be announced later.)

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Programming projects

Implement an intelligent agent for playing Pacman. The project will be divided into three parts, with increasing levels of difficulty.

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Evaluation

  • Written exam (60%)
    • Short questions on the reading assignment will be part of the exam.
  • Programming projects (40%)
    • Project 1: 10%
    • Project 2: 15%
    • Project 3: 15%
    • Programming projects are mandatory for presenting the exam.

Going further

This course is designed as an introduction to the many other courses available at ULiège and (broadly) related to AI, including:

  • INFO8006: Introduction to Artificial Intelligence $\leftarrow$ you are there
  • ELEN0062: Introduction to Machine Learning
  • INFO8004: Advanced Machine Learning
  • INFO8010: Deep Learning
  • INFO8003: Optimal decision making for complex problems
  • INFO0948: Introduction to Intelligent Robotics
  • INFO0049: Knowledge representation
  • ELEN0016: Computer vision
  • ELEN0060: Information and coding theory
  • MATH2022: Large-sample analysis: theory and practice
  • DROI8031: Introduction to the law of robots

???

Mention pre-requisites:

  • programming experience
  • probability theory

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Let's start!