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?
What's the workload like?
What's the text book?
How is my grade determined?
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?
calendar
(Subject to minor revision throughout the term)
Date Topic and slides Readings Material Due weight
Sep. 8 Introduction: administrivia, 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 |
optional readings indicated in square brackets: [Ch. 1.2 | Minsky, Why People Think Computers Can't | Brooks, Intelligence without Reason | Etzioni, Intelligence without Robots | Dennett, Can Machines Think | Social Robots (video) ]
 
Sep. 10 Problem Solving By Search: problem formulation, search strategies, heuristics Ch. 3 |  
Sep. 15 Two-player games: minimax, alpha-beta pruning, position evaluators Ch. 5-5.4 |  
Sep. 17 Intelligence and agency: agent classifications and architectures Ch. 2 | Brooks, A Robust Layered Control System for a Mobile Robot |
Sep. 22 Constraint Satisfaction Problems Ch. 6-6.4  
Sep. 24 Logical Agents: wumpus world, reasoning in propositional logic Ch. 7-7.5  
Sep. 29 Planning Ch. 10-10.2 |  
Oct. 1 in-class game tournament Assignment #1 15%
Oct. 6 Hidden Markov Models Ch. 15-15.3, Eisner lecture and interactive spreadsheet  
Oct. 8 Decision Making: simple and complex decisions, Markov decision problems, utility, value iteration, policy iteration Ch. 16.3, 17-17.4  
Oct. 13 (material deferred to Oct. 15 due to assessment of learning acitivity)    
Oct. 15 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  
Oct. 20 Inductive Learning: decision trees, pruning, ensemble learning, boosting Ch. 18.3 | Sammut et al., Learning To Fly Assignment #2 15%
Oct. 22 Passive Reinforcement Learning: direct utility estimation, adaptive dynamic programming, temporal difference learning Ch. 21-21.2 |  
Oct. 27 (material deferred to Oct. 29 due to assessment of learning acitivity)    
Oct. 29 Active Reinforcement Learning: action-value function, Q-TD, exploratory learning, generalization Ch. 21.3-21.6 | exercise
Nov. 3 Non-parametric models: nearest neighbour, kd-trees, locally sensitive hashing Ch. 18.8 | Mitchell, Instance-based learning (pp. 230-234)  
Nov. 5 k-means and PCA: clustering, component analysis, and dimensionality reduction Turk, Face recognition using eigenfaces  
Nov. 10 Statistical Learning: Bayesian learning, MAP, maximum likelihood Ch. 20-20.2  
Nov. 12 Mixture Models and Expectation Maximization Ch. 20.3 | Assignment #3 15%
Nov. 17 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 |
 
Nov. 19 Neural Network Applications OCHRE applet | exercise
Nov. 24 recurrent neural networks, Boltzmann machines, networks, deep learning LeCun, Deep Learning | Kröse and van der Smagt, Intro to NN Ch. 5 | exercise
Nov. 26 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 |  
Dec. 1, 3 Project Presentations Project 25%
Last updated on 18 November 2015