Intelligent Systems

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.

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 Naïve Bayes

4.2 Neural Networks and Deep Learning

4.3 Reinforcement learning

5 Perception and Communication

5.1 Computer vision

5.2 Natural language understanding

• Assignments 50%
• Exams 30%
• Final project 20%

Grades

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, Springer 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

Week	Topic	Material	Assignments
Feb 1	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
Feb 8	2.1 Search	[Russell10] Chap 3 (slides) [AI-edX] Agents and Search (slides) (video) Search methods (Python notebook)	Assignment 0
Feb 15	2.1 Search	[Russell10] Chap 3 (slides) [AI-edX] A* Search and Heuristics (slides) (video) Search methods (Python notebook)	Assignment 1
Feb 22	2.2 Adversarial search	[Russell10] Chap 5 (slides) [AI-edX] Game Trees: Minimax (slides) (video)	Assignment 2
Mar 8	3.1 Probability	[Russell10] Chap 13 (slides) [AI-edX] Probability (slides) (video)
Mar 16	3.2 Probabilistic reasoning	[Russell10] Chap 14 (slides) [AI-edX] Bayes' Nets: Representation (slides) (video)
Mar 22	3.2 Probabilistic reasoning	[Russell10] Chap 14 (slides) [AI-edX] Bayes' Nets: Inference (slides) (video) [AI-edX]Bayes' Nets: Sampling (slides) (video) [Alp10] Chap 16 (slides)
Apr 5	3.3 Hidden Markov models	[Russell10] Chap 15 (slides) [AI-edX] HMMs(slides) (video) [AI-edX] HMM Filtering(slides) (video) [Alp10] Chap 15 (slides)	Assignment 3
Apr 19	4.1 Naïve Bayes	[Russell10] Chap 20 (slides) [AI-edX] ML: Naive Bayes (slides) (video)
Apr 26	4.2 Neural Networks and Deep Learning	[Russell10] Chap 18 (slides) [AI-edX] ML: Perceptrons (slides) (video) [Alp10] Chap 11 (slides) Quick and dirty introduction to neural networks (IPython notebook) Representation Learning and Deep Learning Tutorial
May 3	4.3 Reinforcement learning	[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)
May 10	4.3 Reinforcement learning	[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)