Difference between revisions of "Syllabus for Machine Learning 10-601B in Spring 2016"
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| M 1/25 || [[10-601 Logistic Regression|Logistic Regression]] || William || || | | M 1/25 || [[10-601 Logistic Regression|Logistic Regression]] || William || || | ||
|- | |- | ||
− | | W 1/27 || [[10-601 | + | | W 1/27 || [[10-601 Linear Regression|Linear Regression]] || William |
|- | |- | ||
+ | | M 2/1 || [[10-601 Perceptrons and Voted Perceptrons|Perceptrons]] and [[10-601 SVMS|SVMs]] || Nina || || | ||
|- | |- | ||
− | | | + | | W 2/3 || Kernels || Nina || || |
− | |||
− | |||
|| HW3: implementing logistic regression and kernel perceptrons || Tianshu, Will | || HW3: implementing logistic regression and kernel perceptrons || Tianshu, Will | ||
|- | |- |
Revision as of 10:01, 12 January 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.
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 kernel perceptrons | 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 | PCA and dimension reduction | William | ||||
M 4/11 | Matrix Factorization and collaborative filtering | William | ||||
W 4/13 | Deep Learning 1 | William | ||||
M 4/18 | Deep Learning 2 | 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.