Difference between revisions of "Syllabus for Machine Learning 10-601 in Fall 2013"
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| W 9/18 || [[10-601 Logistic Regression|Logistic Regression]] || William || HW: Implement two learners | | W 9/18 || [[10-601 Logistic Regression|Logistic Regression]] || William || HW: Implement two learners | ||
|- | |- | ||
− | | M 9/23 | + | | M 9/23 || Linear regression and BackProp || Eric |
|- | |- | ||
− | | W 9/25 | + | | W 9/25 || Neural networks and Deep Belief Networks || Eric || HW |
|- | |- | ||
− | | M 9/30 | + | | M 9/30 || K-NN, Decision Trees, and Kernels || William || |
|- | |- | ||
− | | W 10/2 | + | | W 10/2 || Comparing Classifiers Experimentally || William || HW |
|- | |- | ||
− | | M 10/7 || | + | | M 10/7 || PAC Learning || Eric ''(William out)'' |
|- | |- | ||
− | | W 10/9 || | + | | W 10/9 || Bias-Variance Decomposition || Eric ''(William out)'' |
|- | |- | ||
− | | M 10/14 | + | | M 10/14 || Ensemble Learning Techniques 1 || William |
|- | |- | ||
− | | W 10/16 | + | | W 10/16 || Ensemble Learning Techniques 2 || William |
|- | |- | ||
− | | M 10/21 | + | | M 10/21 || Unsupervised Learning: k-Means and Mixtures || Eric |
|- | |- | ||
− | | W 10/23 | + | | W 10/23 || Unsupervised Learning: Dimensionality Reduction|| Eric |
|- | |- | ||
− | | M 10/28 | + | | M 10/28 || Semi-Supervised Learning || William |
|- | |- | ||
− | | W 10/30 | + | | W 10/30 || Collaborative Filtering and Matrix Factorization || William |
|- | |- | ||
− | | M 11/4 | + | | M 11/4 || Graphical Models 1 || Eric |
|- | |- | ||
− | | W 11/6 | + | | W 11/6 || Graphical Models 2 || Eric |
|- | |- | ||
− | | M 11/11 | + | | M 11/11 || HMMS, Sequences, and Structured Output Prediction || William |
|- | |- | ||
− | | W 11/13 || | + | | W 11/13 || Topic Models|| Eric ''(William out)'' |
|- | |- | ||
− | | M 11/18 | + | | M 11/18 || Topic Models || Eric |
|- | |- | ||
− | | W 11/20 | + | | W 11/20 || Review Session/Special Topics || Eric |
|- | |- | ||
− | | M 11/25 | + | | M 11/25 || Final Exam || |
|- | |- | ||
| W 11/27 || ''No class - Thanksgiving'' || | | W 11/27 || ''No class - Thanksgiving'' || | ||
|- | |- | ||
− | | M 12/2 | + | | M 12/2 || Markov Decision Processes and Reinforcement Learning || Eric |
|- | |- | ||
− | | W 12/4 | + | | W 12/4 || Scalable Learning and Parallelization || William |
|} | |} | ||
Revision as of 10:34, 27 August 2013
This is the syllabus for Machine Learning 10-601 in Fall 2013.
Contents
Prezi Overview of All the Topics in the Course
Schedule
TAs and Eric: For now, let's use the Google Doc Spreadsheet to plan the lectures. Later we can migrate to the wiki schedule below - but it's a little hard to swap things around in the wiki format
Date | Topic | Lecturer | Assignment |
---|---|---|---|
M 9/2 | No class - Labor day | ||
W 9/4 | Overview and Intro to Probability | William | HW: worksheet on probabilities |
M 9/9 | The Naive Bayes algorithm | William | |
W 9/11 | The Perceptron algorithm | William | HW: Implement two learners |
M 9/16 | The Perceptrons, SVMs, and other Margin Classifiers | William | |
W 9/18 | Logistic Regression | William | HW: Implement two learners |
M 9/23 | Linear regression and BackProp | Eric | |
W 9/25 | Neural networks and Deep Belief Networks | Eric | HW |
M 9/30 | K-NN, Decision Trees, and Kernels | William | |
W 10/2 | Comparing Classifiers Experimentally | William | HW |
M 10/7 | PAC Learning | Eric (William out) | |
W 10/9 | Bias-Variance Decomposition | Eric (William out) | |
M 10/14 | Ensemble Learning Techniques 1 | William | |
W 10/16 | Ensemble Learning Techniques 2 | William | |
M 10/21 | Unsupervised Learning: k-Means and Mixtures | Eric | |
W 10/23 | Unsupervised Learning: Dimensionality Reduction | Eric | |
M 10/28 | Semi-Supervised Learning | William | |
W 10/30 | Collaborative Filtering and Matrix Factorization | William | |
M 11/4 | Graphical Models 1 | Eric | |
W 11/6 | Graphical Models 2 | Eric | |
M 11/11 | HMMS, Sequences, and Structured Output Prediction | William | |
W 11/13 | Topic Models | Eric (William out) | |
M 11/18 | Topic Models | Eric | |
W 11/20 | Review Session/Special Topics | Eric | |
M 11/25 | 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 |
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: