Syllabus for Machine Learning 10-601 in Fall 2013
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Contents
Prezi Overview of All the Topics in the Course
Schedule
Note: the days for each lecture will be adjusted - these are the dates from 2013.
Teaching team: also see the Google Doc Spreadsheet
| Date of lecture | Topic | Lecturer | Assignment | |
|---|---|---|---|---|
| M 9/2 | No class - Labor day | |||
| W 9/4 | Overview and Intro to Probability | William | HW1: worksheet on probabilities (due Sept. 13th via BlackBoard) | |
| M 9/9 | The Naive Bayes algorithm | William | ||
| W 9/11 | The Perceptron algorithm | William | HW2:Naive Bayes & Voted Perceptron (due Sept. 18th via Autolab) | |
| M 9/16 | Logistic Regression | William | ||
| W 9/18 | SVMs and Margin Classifiers | William | HW3: Logistic Regression Download: data (due Sept. 25th via Autolab) example solution | |
| M 9/23 | Linear Regression | Eric | ||
| W 9/25 | Neural networks and Deep Belief Networks | Eric | HW4: Linear Regression Download: data (due Oct. 2nd (Before lecture) via Autolab) | |
| M 9/30 | K-NN, Decision Trees, and Rule Learning | William | ||
| W 10/2 | Evaluating and Comparing Classifiers Experimentally | William | HW5: Compare classifiers Download: data1 data2 (due Oct. 9th (Before lecture) via Autolab) Example code: [1] | |
| M 10/7 | PAC Learning | Eric (William out) | ||
| W 10/9 | Bias-Variance Decomposition | Eric (William out) | HW6: PAC and VC dimension | |
| M 10/14 | Ensemble Methods 1 | William | ||
| W 10/16 | Ensemble Methods 2 | William | Project description: [2] and Project Milestone 1 | |
| M 10/21 | Unsupervised Learning: k-Means and Mixtures | Eric | ||
| W 10/23 | Unsupervised Learning: Dimensionality Reduction | Eric |
Project milestone 2: Description, Classifier assignments, and Datasets for Milestone 2 | |
| M 10/28 | Semi-Supervised Learning | William | ||
| W 10/30 | Collaborative Filtering and Matrix Factorization | William | Project milestone 3 Milestone3 Handout | |
| M 11/4 | Graphical Models 1 | Eric | ||
| W 11/6 | Graphical Models 2 | Eric | HW7: Graphical Models (due Nov. 13th before class via BlackBoard) | |
| M 11/11 | HMMS, Sequences, and Structured Output Prediction | William | ||
| W 11/13 | d-separation, Explaining away, and Topic Models | William | Project milestone 4 | |
| M 11/18 | Network Models | Eric | ||
| W 11/20 | Review Session/Special Topics | Eric | ||
| M 11/25 | Not-quite-final Exam | |||
| W 11/27 | No class - Thanksgiving | |||
| M 12/2 | Markov Decision Processes and Reinforcement Learning | Eric | ||
| W 12/4 | Scalable Learning and Parallelization | William | Milestones 5-6 Description | |
| Mon 12/9 | Milestone 5 due | |||
| Tue 12/10 | Milestone 6 (writeup) due |
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:
- 10-601 Introduction to Linear Algebra
- 10-601 Perceptrons and Voted Perceptrons
- 10-601 Voted Perceptrons and Support Vector Machines
Summary:
