Syllabus for Machine Learning 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.

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 Background Test Solutions	Will, Han
M 1/18	Martin Luther King Day
W 1/20	The Naive Bayes algorithm	William	HW2: implementing naive Bayes HW2 Solutions	Travis, Maria
M 1/25	Logistic Regression	William
W 1/27	Linear Regression	William
M 2/1	Perceptrons and Large Margin	Nina
W 2/3	Kernels	Nina	HW3: logistic and linear regression HW3 solutions	Tianshu, Will
M 2/8	Kernelized SVMs and Intro to Neural Networks	Nina
W 2/10	Neural Networks	Nina
M 2/15	AdaBoost	Nina
W 2/17	Generalization and Overfitting: Sample Complexity Results for Supervised Classification	Nina	HW4: SVM, ANN, Boosting	Han, Tianshu
M 2/22	Generalization and Overfitting: Sample Complexity Results for Supervised Classification 2	Nina
W 2/24	Generalization and Overfitting: Sample Complexity Results for Supervised Classification 3 and Midterm Review	Nina
M 2/29	Midterm exam
W 3/2	Unsupervised Learning and Clustering	Nina
M 3/7	Spring break
W 3/9	Spring break
M 3/14	Active Learning	Nina
W 3/16	Semi-Supervised Learning	William	HW5: Active learning and clustering	Travis, Han
M 3/21	Graphical Models 1	William
W 3/23	Graphical Models 2	William
M 3/28	Graphical Models for Sequential Data	William
W 3/30	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	Zichao
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.

Note from William to William and Nina: there's a copy of the old draft, with William's slides and notes, here

Other Lectures

• 10-601 Wrap-up on Linear Classification