Draft schedule for 10-601B in Spring 2016

This is the syllabus for Machine Learning 10-601 in Spring 2016.

Schedule

Teaching team only: also see the Google Doc Spreadsheet. Students should not try and decipher the scribbles and planning notes on this gdoc - use the schedule below. Also - a draft schedule for 10-601B

Schedule for 10-601

Date	Main Topic of Lecture	Lecturer	Assignment	TAs
M 1/11	Course Overview	Nina
W 1/13	Intro to Probability	William	HW1 Probabilities	Will, Han
M 1/18	Martin Luther King Day
W 1/20	The Naive Bayes algorithm	William	HW2: implementing naive Bayes	Travis, Maria
M 1/25	Logistic Regression	William
W 1/27	Linear Regression	William
M 2/1	Perceptrons and SVMs	Nina
W 2/3	Kernels	Nina	HW3: implementing logistic regression and linear regression	Tianshu, Will
M 2/8	Neural Networks and Backprop	Nina
W 2/10	Decision Trees and Rules	Nina
M 2/15	Boosting and Other Ensembles	Nina
W 2/17	Theory 1	Nina	HW4: Theory	Han, Tianshu
M 2/22	Theory 2	Nina
W 2/24	Midterm Review	Nina
M 2/29	Midterm exam
W 3/2	Unsupervised Learning: k-Means and Mixtures	Nina
M 3/7	Semi-Supervised Learning	Nina
W 3/9	Active Learning	Nina	HW5: Active learning and clustering	Travis, Han
M 3/14	Spring break
W 3/16	Spring break
M 3/21	Graphical Models 1	William
W 3/23	Graphical Models 2	William
M 3/28	Graphical Models for Sequential Data	William	HW6: Graphical models	Maria, Renato
M 4/4	Topic Models	William
W 4/6	Deep Learning 1	William
M 4/11	Deep Learning 2	William
W 4/13	PCA and dimension reduction	William	HW7: Deep Learning	TBA
M 4/18	Matrix Factorization and collaborative filtering	William
W 4/20	Reinforcement Learning	Nina
M 4/25	Review	Nina and William
W 4/27	Final exam

To other instructors: if you'd like to use any of the materials found here, you're absolutely welcome to do so, but please acknowledge their ultimate source somewhere.

Other Lectures

• 10-601 Wrap-up on Linear Classification