Date | Topic and slides | Readings | Videos | Material Due weight |
---|---|---|---|---|
Sep. 3 | 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 |
Ch. 1.2-1.3.5 |
Minsky,
Why People Think Computers Can't |
Brooks, Intelligence without Reason |
Etzioni, Intelligence without Robots |
Dennett, Can Machines Think |
Saygin, Turing Test: 50 Years Later |
Merchant, When Artificial Intelligence Is Dumb | Brean, How Do You Teach a Machine Right from Wrong? Addressing the Morality within Artificial Intelligence |
Social Robots | |
Sep. 8 | Problem Solving By Search: problem formulation, search strategies, heuristics | Ch. 3 | | ||
Sep. 10 | Two-player games: minimax, alpha-beta pruning, position evaluators | Ch. 5-5.4 |
Animated Alpha-Beta example |
interactive alpha-beta animation |
Schaeffer et al., Checkers is Solved |
Schaeffer, A Gamut of Games |
Nau, AI Game-Playing Techniques: Are They Useful for Anything Other than Games
|
Patrick Winston. games, minimax and alpha-beta | Francisco Iacobelli. minimax tutorial | |
Sep. 15 | Intelligence and agency: agent classifications and architectures | Ch. 2 | Brooks, A Robust Layered Control System for a Mobile Robot | Allyn, 'The Computer Got It Wrong' | | ||
Sep. 17 | Probability Basics | Ch. 13.2-13.5 | Patrick Winston, lecture | Jeff Miller, tutorial on proportionality | Nando de Freitas, Bayes' Rule | |
Sep. 22 | Hidden Markov Models | Ch. 15-15.3, interactive spreadsheet | Jason Eisner, Probabilities and Language Models | Jeff Miller, part 1 | part 2 | |
Sep. 24 | Decision Making: simple and complex decisions, Markov decision problems, utility, value iteration, policy iteration | Ch. 16.3, 17-17.3 | ||
Sep. 29 | 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 | Vincent, What a machine learning tool tells us about AI bias | | | Jeff Miller, supervised learning | unsupervised learning | |
Oct. 1 | Inductive Learning: decision trees, pruning, ensemble learning, boosting | Ch. 18.3 | Sammut et al., Learning To Fly | Jeff Miller, decision trees | |
Oct. 6 | Non-parametric models: nearest neighbour, kd-trees, locally sensitive hashing | Ch. 18.8 | Mitchell, Instance-based learning (pp. 230-234) | Patrick Winston, lecture | |
Oct. 8 | in-class game tournament | |||
Oct. 13 | Statistical Learning: Bayesian learning, MAP, maximum likelihood | Ch. 20-20.2 | Jeff Miller short tutorial | |
Oct. 15 | Mixture Models and Expectation Maximization | Ch. 20.3 | | Jeff Miller, EM algorithm | Why EM makes sense, part 1 | part 2 | |
Oct. 20 | Passive Reinforcement Learning: direct utility estimation, adaptive dynamic programming, temporal difference learning | Ch. 21-21.2 |
Sutton & Barto, Reinforcement Learning, Ch. 1 |
Kaelbling et al., Reinforcement Learning: A Survey |
Tesauro, Temporal Difference Learning and TD-Gammon |
reinforcement learning applet | exercise |
||
Oct. 22 | 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 |
[
Mnih et al., Playing Atari with Deep Reinforcement Learning
| ||
Oct. 27 | k-means and PCA: clustering, component analysis, and dimensionality reduction | PCA and Self-organizing maps | | Jeff Miller, short tutorial | |
Oct. 29 | Constraint Satisfaction Problems | Ch. 6-6.4 | ||
Nov. 3 | in-class game tournament | |||
Nov. 5 | Logical Agents: wumpus world, reasoning in propositional logic | Ch. 7-7.5 | ||
Nov. 10 | Planning | Ch. 10-10.2 | Sussman Anomaly | Dolphin planning | |
Nov. 12 | 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. 17 | Neural Network Applications | Pomerleau, Efficient
Training of Artificial Neural Networks for Autonomous Navigation | OCHRE applet |
Rowley, Baluja & Kanade, Neural-Network-Based Face Detection |
Fels, An Adaptive Interface that Maps Hand Gestures to Speech
|
||
Nov. 19 | 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 |
Mitchell, Chapter 9 |
AAAI GA resources site |
genetic algorithms applet | genetic programming applet
|
||
Nov. 24 | recurrent neural networks, Boltzmann machines, networks, deep learning | LeCun, Deep Learning |
Kröse and van der Smagt, Intro to NN Ch. 5 |
Hopfield network applet | Schraudolph & Cummins Introduction to NN Lecture 3 |
Hopfield, Neural Networks and Physical Systems with Emergent Collective Computational Abilities
|
Hugo Larochelle, intro to deep learning | |
Nov. 26 | Project Presentations | |||
Dec. 1 | Project Presentations | Project |