Course description
Instructor
Fabio A. González
Maestría en Ingeniería de Sistemas y Computación
Universidad Nacional de Colombia
Course goal
Intelligent systems study the development of intelligent or rational agents. A rational agent must be able to act in order to maximize the best expected upcome. The goal of the course is to study the theory and methods that allow to build rational agents under different conditions.
Course topics
1 Introduction
2 Problem Solving
2.1 Search
2.2 Adversarial search
2.3 Constraint satisfaction problems
3 Uncertain Reasoning
3.1 Probability
3.2 Probabilistic reasoning
3.3 Hidden Markov models
3.4 Markov decision problems
4 Learning
4.1 Reinforcement learning
4.2 Naïve Bayes
4.3 Neural Networks and Deep Learning
Evaluation and grading policy
- Assignments 50%
- Exams 30%
- Final project 20%
Grades (to access, you need to be authenticated with your UNAL credentials)
Course resources
References and resources
- [Russell10] Russell, S and Norvig, P. 2010 Artificial Intelligence: a Modern Approach, 3rd Ed, Prentice-Hall
- [Alp10] Alpaydin, E. 2010 Introduction to Machine Learning, 2Ed. The MIT Press.
- [AI-edX] CS188x_1 Artificial Intelligence, UC Berkley, edX, Spring 2016
- [Alp10] Alpaydin, E. Introduction to Machine Learning, 2Ed. The MIT Press, 2010
- [Sutton98] Sutton, R. and Barto, A. Reinforcement Learning: An Introduction, The MIT Press, 1998
- [aima-python] Python code for the book Artificial Intelligence: A Modern Approach, GitHub repository
Course schedule
| Week | Topic | Material | Assignments |
|---|---|---|---|
| Aug 15 | 1. Introduction | [Russell10] Chap 1 (slides) and 2 (slides) [AI-edX] Introduction to AI (slides) (video) | [AI-edX] Math Self-Diagnostic [AI-edX] P0: Tutorial |
| Aug 22 | 2.1 Search | [Russell10] Chap 3 (slides) [AI-edX] Agents and Search (slides) (video) Search methods (Python notebook) | |
| Aug 29 | 2.1 Search | [Russell10] Chap 3 (slides) [AI-edX] A* Search and Heuristics (slides) (video) Search methods (Python notebook) | Assignment 1 |
| Sep 5-12 | 3.1 Probability | [Russell10] Chap 13 (slides) [AI-edX] Probability (slides) (video) | |
| Sep 19-26 | 3.2 Probabilistic reasoning | [Russell10] Chap 14 (slides) [AI-edX] Bayes' Nets: Representation (slides) (video) [AI-edX] Bayes' Nets: Inference (slides) (video) | |
| Oct 3 | 3.2 Probabilistic reasoning | [Russell10] Chap 14 (slides) [AI-edX]Bayes' Nets: Sampling (slides) (video) [Alp10] Chap 16 (slides) | |
| Oct 3-10 | 3.3 Hidden Markov models | [Russell10] Chap 15 (slides) [AI-edX] Markov Models (slides) and HMMs (slides) (video) [AI-edX] HMM Filtering(slides) (video) [Alp10] Chap 15 (slides) | Assignment 2 |
| Nov 7-14 | 4.1 Reinforcement learning | Video class Nov 9 Video class Nov 14 [Russell10] Chap 17 [AI-edX] Markov Decision Processes (slides) (video) [AI-edX] Markov Decision Processes II(slides) (video) [aima-python] Markov Decision Processes (notebook) (code) | |
| Nov 14-21 | 4.1 Reinforcement learning | Video class Nov 16 Video class Nov 21 [Russell10] Chap 21 [AI-edX] Reinforcement Learning (slides) (video) [AI-edX] Reinforcement Learning II(slides) (video) [Alp10] Chap 18 (slides) [Sutton98] (html) (pdf) [aima-python] Reinforcement Learning (notebook) (code) Dissecting Reinforcement Learning | |
| Nov 28 | 4.2 Naïve Bayes | Video class Nov 30 [Russell10] Chap 20 (slides) [AI-edX] ML: Naive Bayes (slides) (video) | Assignment 3 |
| Quiz 4 (Feb 1st) Exam (Feb 8th)) Final Project (Feb 13th)) |