Syllabus for Machine Learning 10-601B in Spring 2016

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


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 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 Large Margin Nina
W 2/3 Kernels Nina HW3: logistic and linear regression 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 HW4 code Han, Tianshu
M 2/22 Generalization and Overfitting: Sample Complexity Results for Supervised Classification 2 Nina
W 2/24 Model Selection and Midterm Review Nina
M 2/29 Midterm exam
W 3/2 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 3 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 In-class 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