Artificial Intelligence - ECSE 526
course description
This course will introduce students to Artificial Intelligence (AI), beginning from historical and philosophical perspectives, progressing through a number of core topics from classical AI, and then dealing extensively with various areas of machine learning. The latter topic will emphasize connectionist architectures (artificial neural networks) and evolutionary computing approaches.
questions
What will I learn?
Is this course for me?
Can I enroll in this course if I've already taken a CS course in AI or ML?
What's the workload like?
What computers do I use to run my assignments?
What's the text book?
How is my grade determined?
What?! I assess my peers (and my own work)?
What are the in-class and homework activities?
What are the due dates and policy on late work?
Where's that line about academic integrity?
Further important information about the course is available from the course guide.
calendar
(Subject to minor revision throughout the term)
Date Topic and slides Readings Videos Material Due weight
Jan. 7 Introduction: definitions, historical and philosophical underpinnings, and what this course is about Ch 1.1, | Littman, 'Rise of the Machines' is Not a Likely Future | Social Robots  
Jan. 9 Problem Solving By Search: problem formulation, search strategies, heuristics Ch. 3 |    
Jan. 14 Two-player games: minimax, alpha-beta pruning, position evaluators Ch. 5-5.4 | Patrick Winston. minimax lecture | Francisco Iacobelli. minimax tutorial  
Jan. 16 Intelligence and agency: agent classifications and architectures Ch. 2 | Brooks, A Robust Layered Control System for a Mobile Robot |  
Jan. 21 Principles of Machine Learning: regression, overfitting, training, validation, and test sets, leave-one-out cross-validation, k-fold cross-validation, gradient descent Ch 18-18.2 | Domingo, A Few Useful Things to Know about Machine Learning Jeff Miller, supervised learning | unsupervised learning  
Jan. 23 Probability Basics Ch. 13.2-13.5 Jeff Miller, part 1 | part 2 | Patrick Winston, lecture | Jeff Miller, tutorial on proportionality | Nando de Freitas, Bayes' Rule  
Jan. 28 Non-parametric models: nearest neighbour, kd-trees, locally sensitive hashing Ch. 18.8 | Mitchell, Instance-based learning (pp. 230-234) Patrick Winston, lecture Assignment #1
Jan. 30 in-class game tournament
Feb. 4 Statistical Learning: Bayesian learning, MAP, maximum likelihood Ch. 20-20.2 Jeff Miller short tutorial  
Feb. 6 Hidden Markov Models Ch. 15-15.3, interactive spreadsheet Jason Eisner, Probabilities and Language Models | Jeff Miller, part 1 | part 2  
Feb. 11 Decision Making: simple and complex decisions, Markov decision problems, utility, value iteration, policy iteration Ch. 16.3, 17-17.4    
Feb. 13 Inductive Learning: decision trees, pruning, ensemble learning, boosting Ch. 18.3 | Sammut et al., Learning To Fly Jeff Miller, decision trees  
Feb. 18 Passive Reinforcement Learning: direct utility estimation, adaptive dynamic programming, temporal difference learning Ch. 21-21.2 |  
Feb. 20 Active Reinforcement Learning: action-value function, Q-TD, exploratory learning, generalization Ch. 21.3-21.6 | Mahadevan & Connell, Automatic Programming of Behavior-based Robots using Reinforcment Learning |   exercise
Feb. 25 k-means and PCA: clustering, component analysis, and dimensionality reduction PCA and Self-organizing maps | Turk, Face recognition using eigenfaces Jeff Miller, short tutorial  
Feb. 27 Mixture Models and Expectation Maximization Ch. 20.3 | Jeff Miller, EM algorithm | Why EM makes sense, part 1 | part 2  
Mar. 3 Study Week
Mar. 5 Study Week
Mar. 10 Constraint Satisfaction Problems Ch. 6-6.4    
Mar. 12 in-class game tournament
Mar. 17 Logical Agents: wumpus world, reasoning in propositional logic Ch. 7-7.5    
Mar. 19 Planning Ch. 10-10.2 | Sussman Anomaly Dolphin planning  
Mar. 24 Intro to Artificial Neural Networks Ch. 18.7 | Mitchell, Artificial Neural Networks (4.1-4.6) |
Schraudolph & Cummins, Introduction to NN | Tveter, BackProp Basics (or available as PDF here | Can animals deduce? (video) | Rowley, Baluja & Kanade, Neural-Network-Based Face Detection | Fels, An Adaptive Interface that Maps Hand Gestures to Speech |
Welch Labs, playlist  
Mar. 26 Neural Network Applications Pomerleau, Efficient Training of Artificial Neural Networks for Autonomous Navigation | OCHRE applet |   exercise
Mar. 31 Evolutionary Computing: genetic algorithms, genetic programming, evolving neural networks Nolfi et al Phenotypic Plasticity in Evolving NN | Bling, MarI/O (video) | Koza, Breeding Populations of Programs to Solve Problems in AI |    
Apr. 2 recurrent neural networks, Boltzmann machines, networks, deep learning LeCun, Deep Learning | Kröse and van der Smagt, Intro to NN Ch. 5 | Hugo Larochelle, intro to deep learning exercise
Apr. 7 Project Presentations  
Apr. 9 Project Presentations Project 25%
Last updated on 14 January 2020