Skip to content

Latest commit

 

History

History
151 lines (98 loc) · 3.7 KB

outline.md

File metadata and controls

151 lines (98 loc) · 3.7 KB
Raw

class: middle, center, title-slide

Introduction to Artificial Intelligence

Fall 2018



Prof. Gilles Louppe
g.louppe@uliege.be

???

check Nando's https://www.youtube.com/watch?v=z8937RleAZo

Logistics

This course is given by:

Feel free to contact any of us for help!


.center[ .circle[![](figures/outline/gilles.jpg)]   .circle[![](figures/outline/antoine.jpg)]   .circle[![](figures/outline/samy.png)] ]

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).
  • Slightly different from previous years.

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

.center.width-80[]

Textbook

.center.width-30[]

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.

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.

Outline

  • Lecture 1: Foundations
  • 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

Projects

Reading assignment

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

.center.width-40[]

class: middle

Programming project

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

  • Eat as many dots as possible
  • Eat as many dots as possible, while not getting killed by ghosts (deterministic)
  • Eat as many dots as possible, while not getting killed by ghosts (stochastic)

.center.width-30[]

Evaluation

  • Exam (60%)
  • Reading assignment (10%)
  • Programming project (30%)

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 related to AI, including:

  • 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
  • DROI8031: Introduction to the law of robots

class: end-slide, center count: false

Let's start!