Lecture Schedule

Apr 1 Course Overview Slides
Apr 3 A Geometric View of Linear Algebra. Notes
Apr 8 Finish up A Geometric View of Linear Algebra. See Notes for 4/3 lecture.
Apr 10 Nearest Neighbor Classification. Notes
Apr 15 More on Nearest Neighbor Classification. See Notes for 4/10 lecture.
Apr 17 Decision Trees. Notes
Apr 22 Decision Trees. See Notes for 4/17 lecture.
Apr 24 Finish up Decision Trees. See Notes for 4/17 lecture. Notes 1 Notes 2
Apr 29 Decision Tree Example. Linear Classification and Perceptron. Notes
May 1 Perceptron. See Notes for 4/29 lecture.
May 6 Midterm
May 8 Midterm Solutions Discussion. Perceptron. See Notes for 4/29 lecture.
May 13 Voted and Averaged Perceptron . See Notes for 4/29 lecture.
May 15 Finish up Linear Classification. Kernels. Linear Classification Notes

Kernels Notes

May 20 Kernels. See 4/15 notes.
May 22 Finish up Kernels. Start with Bias and Variance. Bias and Variance Notes
May 27 Kernels Problem Session.
May 29 Ensemble Learning. Notes
Jun 2 Finish up Boosting. Viola and Jones. Slides
Jun 4 Tips for Practical ML. Slides