Syllabus for Machine Learning 10-601 in Fall 2014

This is the syllabus for Machine Learning 10-601 in Fall 2014.

Schedule

Schedule for 10-601 in Fall 2013

Date of lecture	Topic	Lecturer	Assignment/Notes
Tues 9/2	Overview and Intro to Probability	William
Thurs 9/4	Classification and K-NN	William	slides will be updated
Tues 9/9	Decision Trees, and Rule Learning	William	slides will be updated
Thurs 9/11	The Naive Bayes algorithm	William
Tues 9/16	Linear Regression	William	slides will be updated
Thurs 9/18	Logistic Regression	William
(Mon +) Tues 9/23	The Perceptron algorithm	William	William's also lecturing in Ziv's class on Mon
Thurs 9/25	Neural networks and Deep Belief Networks	William	slides will be updated
(Mon +) Tues 9/30	SVMs and Margin Classifiers 1	Ziv	Ziv's also lecturing in his class on Mon
(Wed +) Thurs 10/1-2	SVMs and Margin Classifiers 2	Ziv	Ziv's also lecturing in his class on Wed
Tues 10/7	Evaluating and Comparing Classifiers Experimentally	William
Thus 10/9	PAC Learning	William
(Mon +) Tues 10/14	Bias-Variance Decomposition	William	William's also lecturing in Ziv's class on Mon
Thurs 10/16	Ensemble Methods 1, Ensemble Methods 2	William	slides to be updated
Tues 10/21	Unsupervised Learning: k-Means and Mixtures	Bhavana or Ziv
Thus 10/23	Unsupervised Learning: Dimensionality Reduction	Bhavana or Ziv
Tues 10/28	Review session	William	slides to be posted
Thurs 10/30	Mid-term Exam		TBA: room and/or time may be different
(Mon +) Tues 11/4	Graphical Models 1	Ziv	Ziv's also lecturing in his class on Mon
(Wed +) Thurs 11/6	Graphical Models 2	Ziv	Ziv's also lecturing in his class on Wed
(Mon +) Tues 11/11	HMMS and Sequences	Ziv	Ziv's also lecturing in his class on Mon
(Wed +) Thus 11/13	Matrix Factorization and Topic Models	William	William's also lecturing in Ziv's class on Wed, slides to be updated
(Mon +) Tues 11/18	Network Models	William	William's also lecturing in Ziv's class on Mon
(Wed +) Thurs 11/20	Semi-supervised learning	William	William's also lecturing in Ziv's class on Wed
(Mon +) Tues 11/25	Scalable Learning and Parallelization	William	William's also lecturing in Ziv's class on Mon
Thurs 11/27	No class - Thanksgiving
(Mon +) Tues 12/2	Learning and NLP	William
(Wed +) Thurs 12/4	Learning and Biology	William

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.

Section-by-Section

Linear Classifiers

A probabilistic view of linear classification:

• 10-601 Introduction to Probability
• 10-601 Naive Bayes
• 10-601 Logistic Regression

Another view of classification:

• 10-601 Introduction to Linear Algebra
• 10-601 Perceptrons and Voted Perceptrons
• 10-601 Voted Perceptrons and Support Vector Machines

Summary:

• 10-601 Wrap-up on Linear Classification