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.
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.
(Subject to minor revision throughout the term)
Date Topic and slides Readings Videos Material Due weight
Sep. 5 Introduction: definitions, historical and philosophical underpinnings, and what this course is about Ch 1.1, 1.3-1.3.5 | Littman, 'Rise of the Machines' is Not a Likely Future | Social Robots  
Sep. 7 Problem Solving By Search: problem formulation, search strategies, heuristics Ch. 3 |    
Sep. 12 Two-player games: minimax, alpha-beta pruning, position evaluators Ch. 5-5.4 | Patrick Winston. minimax lecture | Francisco Iacobelli. minimax tutorial  
Sep. 14 Intelligence and agency: agent classifications and architectures Ch. 2 | Brooks, A Robust Layered Control System for a Mobile Robot |  
Sep. 19 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  
Sep. 21 Probability Basics (self-guided study session) Ch. 13.2-13.5 Jeff Miller, part 1 | part 2 | Patrick Winston, lecture | Jeff Miller, tutorial on proportionality | Nando de Freitas, Bayes' Rule  
Sep. 26 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 (qualifying)
Sep. 28 in-class game tournament Assignment #1 (final) 15%
Oct. 3 Statistical Learning: Bayesian learning, MAP, maximum likelihood Ch. 20-20.2 Jeff Miller short tutorial  
Oct. 5 Hidden Markov Models Ch. 15-15.3, interactive spreadsheet Jason Eisner, Probabilities and Language Models | Jeff Miller, part 1 | part 2  
Oct. 10 Seminar: Building AI-powered Experiences at Facebook Scale      
Oct. 12 Decision Making: simple and complex decisions, Markov decision problems, utility, value iteration, policy iteration Ch. 16.3, 17-17.4    
Oct. 17 Inductive Learning: decision trees, pruning, ensemble learning, boosting Ch. 18.3 | Sammut et al., Learning To Fly Jeff Miller, decision trees  
Oct. 19 Passive Reinforcement Learning: direct utility estimation, adaptive dynamic programming, temporal difference learning Ch. 21-21.2 |   Assignment #2 15%
Oct. 24 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
Oct. 26 k-means and PCA: clustering, component analysis, and dimensionality reduction PCA and Self-organizing maps | Turk, Face recognition using eigenfaces Jeff Miller, short tutorial  
Oct. 31 Mixture Models and Expectation Maximization Ch. 20.3 | Jeff Miller, EM algorithm | Why EM makes sense, part 1 | part 2  
Nov. 2 Constraint Satisfaction Problems Ch. 6-6.4    
Nov. 7 in-class game tournament Assignment #3 15%
Nov. 9 Logical Agents: wumpus world, reasoning in propositional logic Ch. 7-7.5    
Nov. 14 Planning Ch. 10-10.2 | Sussman Anomaly Dolphin planning  
Nov. 16 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  
Nov. 21 Neural Network Applications Pomerleau, Efficient Training of Artificial Neural Networks for Autonomous Navigation | OCHRE applet |   exercise
Nov. 23 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 |    
Nov. 28 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
Nov. 30 Project Presentations  
Dec. 5 Project Presentations Project 25%
Last updated on 22 November 2017