Schedule

Week	Date	Topic	Assignments / Reading
1	9/4	Intro [slides]	HW 0 part 1 due Tue 9/9 matlab exercise due Fri 9/12 by moodle
	9/5	Fourth hour: Optional calculus review [slides]
2	9/9	Linear regression in one variable [slides]	Read pp. 1–7 of Ng handout
	9/11	Gradient descent for linear regression [slides]
	9/12	Fourth hour (required): Intro to MATLAB	HW 1 due Fri 9/19 [problem_3.m] [moodle]
3	9/16	Linear algebra review [slides] [demo.m]
	9/18	Multivariate Linear Regression [slides]
	9/19	Fourth hour: MATLAB help, HW1 (optional)	HW 2 pdf HW 2 files Exercises due Fri 9/26 [moodle] Problems due Wed 10/1 [moodle]
4	9/23	Geometry of High-Dimensional Linear Functions
	9/25	Normal Equations and Vectorized Gradient Descent
	9/26	Optional Fourth Hour: HW 2, linear algebra
5	9/30	Feature Normalization / Logistic Regression [slides]
	10/2	Logistic Regression	HW 3 pdf HW 3 files due Wed 10/15 [moodle]
6	10/7	Non-linearity, Overfitting, Regularization [slides]
	10/9	Multiclass Classification [slides] Cross-validation [slides]
7	10/14	No Class: Mid-Semester Break
	10/16	Guest Lecture: Weka Tutorial by Kevin Winner	Quiz on moodle, due Tues 10/12
	10/17	No fourth hour: take-home quiz
8	10/21	Support Vector Machines [slides]	Reading: Ng handout
	10/23	Kernel SVMs [slides] [demos]	HW4 pdf HW4 files due Fri 10/31 [moodle]
9	10/28	Gaussian Kernel, Intro to Neural networks	Quiz 1 returned [soln]
	10/30	Neural networks [slides] [notes]	Project guidelines
10	11/4	Backprop
	11/6	Movie Recommendations [slides] [notes] [code and data]	Project proposal due ~~Nov 7~~ Nov 10 at midnight [moodle]
11	11/11	Movie recommendations
	11/13	Principal Components Analysis (PCA) [slides] [demos]
	11/14	Probability review	Stanford probability review handout Movie predictions due Nov 14 at 11:55pm [moodle] Work on projects
12	11/18	Bayesian Reasoning and Naive Bayes [slides]
	11/20	Naive Bayes Discriminative vs. Generative Models	Ng handout on generative models Ng and Jordan paper on discriminative vs. generative models Work on projects!
13	11/25	Clustering, K-means, Mixture of Gaussians [slides]	Project milestone due Nov 26 at noon [moodle]
	11/27	No class: Thanksgiving
14	12/2	No class	Take-home quiz
	12/4	Guest Lecture: Marc Maier from MassMutual Data Science	Work on projects. Skype office hours.
15	12/9	No class
	12/10	Final presentations from 9-11am
16		FINALS	Project report due Dec 16 at noon