Syllabus for CS 194-10, Fall 2011
Introduction to Artificial Intelligence

Subject to change; due dates are approximate until the assignment is posted.
Assignments are due at midnight on the date indicated.
Lecture Topic ReadingsAssignments
Aug 25Intro: what is machine learning? Basic concepts of supervised learning with examples Review material for Week 1 discussion section,
Readings for Week 1
A0: Linear algebra, optimization, probability (due 9/2)
Aug 30Linear regression, least squares Readings for Week 2
Sep 1Linear regression contd.; application (global seismic monitoring) A1: Predicting travel times for seismic waves through the Earth (due 9/9)
6Machine learning methodology: learning curves, overfitting, regularization, cross-validation, feature selection Readings for Week 3
8Classification, 0/1 loss, linear classifiers, SVMs A2: Classification of seismic wave types using SVMs (due 9/19)
13Logistic regression Readings for Week 4
15Kernelization of SVMs and other models
20Decision tree learning Readings for Week 5A3: Decision tree and ensemble learning applied to seismic phase classification (due 10/3)
22Ensemble learning methods (bagging, boosting, etc.)
27Instance-based methods (k-nearest-neighbor, interpolation, etc.) Readings for Week 6
29Multilayer perceptrons ("neural networks"), gradient-based optimization, applications
Oct 4Instance-based learning contd.: distance metrics and efficient indexing Readings for Week 7
11Theoretical analysis: generalization error bounds, regret bounds for online learning Readings for Week 8
13Probabilistic methods: ML, MAP, Bayesian learning, naive Bayes, "bag-of-" models A4: Spam filtering with Naive Bayes (due 10/21)
18Gaussian discriminants; logistic regression revisited Readings for Week 9
20Special presentation: Big data in Groupon A5: Credit scoring with Gaussian discriminants and logistic regression (due 10/28)
25Bayesian regression Readings for Week 10
27Density estimation: kernel density estimation, mixture models A6: Estimating earthquake probabilities (due 11/9)
Nov 1K-means, EM Readings for Week 11
3Bayes nets: representation
8Bayes nets: inference, learning Readings for Week 12
10Bayes net learning contd. A7: Bayes net for car insurance (due 11/20)
15Time series models (Markov processes, n-grams, AR models) Readings for Week 13
17Time series models contd. (HMMs, dynamic Bayes nets)
22Learning in computer vision Readings for Week 14
29Sequential analysis, bandits, active learning Readings for Week 15
Dec 1Summary, current and future developments
16Final (7pm - 10pm) Location TBD

Return to CS 194-10 home page