Note about recorded lectures: you need at least a DSL connection. If you choose to watch the standalone WMV, you'll want to download and follow along on the slides manually. If you don't have Windows Media Viewer, try VLC.

Date	Lecture	Reading	Assignments
1/10	Introduction, Learning, Representation, Supervised Learning, k-Nearest Neighbors, Instance-Based Learning Lecture 1 PPT (1.1 MB) Lecture 1 PDF (1.2 MB) Watch the recorded lecture Watch just the .wmv (no slides, wider field of view) - Video: Real-Time Expression Classification - Video: DARPA Grand Challenge, MIT; more Urban Challenge videos - 1-NN/Voronoi diagram applet	Alpaydin Ch. 1, 2.1, 2.4-2.9 Mitchell p. 230-236 (class handout) Reading Questions	Homework 1 assigned Due January 15
1/17	Decision Trees, Rule Learning, Evaluation 1, Weka Lecture 2 PPT (1.3 MB) Lecture 2 PDF (1.2 MB) Watch the recorded lecture Watch just the .wmv - PredictionWorks browsable decision trees - Intractive demo of learning decision trees	Alpaydin Ch. 9.1-9.4 and 14.1-14.3, Weka Manual (v. 3.6.0); read pages 25-27, 33-35, 39-42, 48-49 Reading Questions	Homework 2 assigned Due January 29
1/20	Deadline to add classes
1/24	Support Vector Machines, Evaluation 2 Lecture 3 PPT (1.2 MB) Lecture 3 PDF (1.2 MB) Watch the recorded lecture Watch just the .wmv - SVM Tutorial (PDF) by Andrew Moore (I recommend pages 1-11, 13-16, 20-22, 32) - Mapping 2D data into 3D (video) - SVM applet	Alpaydin Ch. 10.1-10.4, 10.6, 10.9, and 14.7 Reading Questions	Project proposal due
1/31	Neural Networks Lecture 4 PPT (688 KB) Lecture 4 PDF (768 KB) Watch the recorded lecture Watch just the .wmv - Ball balancing demo - Digit recognition demo	Alpaydin Ch. 11.1-11.8 Reading Questions	Homework 3 assigned Due February 12
2/7	Midterm Exam, Probability Review, Bayesian Learning Lecture 5 PPT (848 KB) Lecture 5 PDF (755 KB) Watch the recorded lecture Watch just the .wmv - Midterm Solution	Alpaydin Appendix A and Ch. 3.1, 3.2, 3.7, 3.9 Mitchell p. 177-179 (class handout) Reading Questions	---
2/14	Note: class is in E&T A331 Parametric Methods Lecture 6 PPT (856 KB) Lecture 6 PDF (850 KB) Lecture not recorded Post-midterm conferences	Alpaydin Ch. 4.1-4.5 Reading Questions	Homework 4 assigned Due February 26
2/19	Deadline to drop classes
2/21	Clustering Lecture 7 PPT (2.2 MB) Lecture 7 PDF (1.8 MB) Watch the recorded lecture Watch just the .wmv - K-means applet - EM applet	Alpaydin Ch. 7.1-7.4, 7.8 Reading Questions	---
2/28	Reinforcement Learning Lecture 8 PPT (905 KB) Lecture 8 PDF (1 MB) Watch the recorded lecture Watch just the .wmv - How TD-Gammon works and some of its discoveries for better plays (written by Gerald Tesauro) - More on TD-Gammon with some more recent game results (by Rich Sutton)	Alpaydin Ch. 16.1-16.5 Reading Questions	---
3/7	Ensemble Learning Lecture 9 PPT (931 KB) Lecture 9 PDF (1 MB) Watch the recorded lecture Watch just the .wmv - Autonomous helicopters at Stanford	Alpaydin Ch. 15.1-15.5	Homework 5 assigned Due March 19
3/14	Review, Project Presentations Watch the recorded lecture Watch just the .wmv	---	Final Project due
3/21	No final exam		Homework 5 due (March 19)