Difference between revisions of "Syllabus for Machine Learning 10-601B in Spring 2016"

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Revision as of 16:34, 6 January 2016

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

Schedule

In progress....

Teaching team: also see the Google Doc Spreadsheet

Schedule for 10-601
Date Main Topic of Lecture Lecturer Assignment TAs
M 1/11 10-601 Course Overview Nina
W 1/13 Intro to Probability William HW1 will be similar to this 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 Perceptrons and SVMs William
M 2/1 Kernels Nina
W 2/3 Linear Regression Nina HW3: implementing logistic regression and kernel perceptrons Tianshu, Will
M 2/8 10-601 Neural Nets 1 Nina
W 2/10 10-601 Neural Nets 2 Nina
M 2/15 10-601 Ensembles Nina
W 2/17 Theory 1 Nina HW4: Theory
M 2/22 Theory 2 Nina
W 2/24 10-601 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 10-601 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 3 William HW6: Graphical models (Maria, Renato)
M 4/4 Topic Models William
W 4/6 Dimension Reduction William
M 4/11 Collaborative Filtering and Matrix Factorization William
W 4/13 Deep Learning 1 William
M 4/18 Deep Learning 2 William
W 4/20 Reinforcement Learning Nina
M 4/22 Review Nina and William
W 4/24 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.

Section-by-Section

Linear Classifiers

A probabilistic view of linear classification:

Another view of classification:

Summary:

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