Difference between revisions of "Syllabus for Machine Learning 10-601 in Fall 2013"
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| M 9/9 || [[10-601 Naive Bayes|The Naive Bayes algorithm]] || William || | | M 9/9 || [[10-601 Naive Bayes|The Naive Bayes algorithm]] || William || | ||
|- | |- | ||
| − | | W 9/11 || [[10-601 Perceptrons and Voted Perceptrons|The Perceptron algorithm]] || William || HW: | + | | W 9/11 || [[10-601 Perceptrons and Voted Perceptrons|The Perceptron algorithm]] || William || HW: Implementation |
|- | |- | ||
| M 9/16 || The Perceptrons, SVMs, and other Margin Classifiers || William || | | M 9/16 || The Perceptrons, SVMs, and other Margin Classifiers || William || | ||
|- | |- | ||
| − | | W 9/18 || [[10-601 Logistic Regression|Logistic Regression]] || William || HW: | + | | W 9/18 || [[10-601 Logistic Regression|Logistic Regression]] || William || HW: Implementation |
|- | |- | ||
| M 9/23 || Linear regression and BackProp || Eric | | M 9/23 || Linear regression and BackProp || Eric | ||
|- | |- | ||
| − | | W 9/25 || Neural networks and Deep Belief Networks || Eric || HW | + | | W 9/25 || Neural networks and Deep Belief Networks || Eric || HW: Implementation |
|- | |- | ||
| M 9/30 || K-NN, Decision Trees, and Kernels || William || | | M 9/30 || K-NN, Decision Trees, and Kernels || William || | ||
|- | |- | ||
| − | | W 10/2 || Comparing Classifiers Experimentally || William || HW | + | | W 10/2 || Comparing Classifiers Experimentally || William || HW: Experimentation |
|- | |- | ||
| M 10/7 || PAC Learning || Eric ''(William out)'' | | M 10/7 || PAC Learning || Eric ''(William out)'' | ||
|- | |- | ||
| − | | W 10/9 || Bias-Variance Decomposition || Eric ''(William out)'' | + | | W 10/9 || Bias-Variance Decomposition || Eric ''(William out)'' || HW: |
|- | |- | ||
| M 10/14 || Ensemble Learning Techniques 1 || William | | M 10/14 || Ensemble Learning Techniques 1 || William | ||
|- | |- | ||
| − | | W 10/16 || Ensemble Learning Techniques 2 || William | + | | W 10/16 || Ensemble Learning Techniques 2 || William || Project milestone |
|- | |- | ||
| M 10/21 || Unsupervised Learning: k-Means and Mixtures || Eric | | M 10/21 || Unsupervised Learning: k-Means and Mixtures || Eric | ||
|- | |- | ||
| − | | W 10/23 || Unsupervised Learning: Dimensionality Reduction|| Eric | + | | W 10/23 || Unsupervised Learning: Dimensionality Reduction|| Eric || Project milestone |
|- | |- | ||
| M 10/28 || Semi-Supervised Learning || William | | M 10/28 || Semi-Supervised Learning || William | ||
|- | |- | ||
| − | | W 10/30 || Collaborative Filtering and Matrix Factorization || William | + | | W 10/30 || Collaborative Filtering and Matrix Factorization || William || Project milestone |
|- | |- | ||
| M 11/4 || Graphical Models 1 || Eric | | M 11/4 || Graphical Models 1 || Eric | ||
|- | |- | ||
| − | | W 11/6 || Graphical Models 2 || Eric | + | | W 11/6 || Graphical Models 2 || Eric || Project milestone |
|- | |- | ||
| M 11/11 || HMMS, Sequences, and Structured Output Prediction || William | | M 11/11 || HMMS, Sequences, and Structured Output Prediction || William | ||
|- | |- | ||
| − | | W 11/13 || Topic Models|| Eric ''(William out)'' | + | | W 11/13 || Topic Models|| Eric ''(William out)'' || Project milestone |
|- | |- | ||
| M 11/18 || Topic Models || Eric | | M 11/18 || Topic Models || Eric | ||
|- | |- | ||
| − | | W 11/20 || Review Session/Special Topics || Eric | + | | W 11/20 || Review Session/Special Topics || Eric |
|- | |- | ||
| M 11/25 || Final Exam || | | M 11/25 || Final Exam || | ||
Revision as of 10:38, 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: Implementation |
| M 9/16 | The Perceptrons, SVMs, and other Margin Classifiers | William | |
| W 9/18 | Logistic Regression | William | HW: Implementation |
| M 9/23 | Linear regression and BackProp | Eric | |
| W 9/25 | Neural networks and Deep Belief Networks | Eric | HW: Implementation |
| M 9/30 | K-NN, Decision Trees, and Kernels | William | |
| W 10/2 | Comparing Classifiers Experimentally | William | HW: Experimentation |
| M 10/7 | PAC Learning | Eric (William out) | |
| W 10/9 | Bias-Variance Decomposition | Eric (William out) | HW: |
| M 10/14 | Ensemble Learning Techniques 1 | William | |
| W 10/16 | Ensemble Learning Techniques 2 | William | Project milestone |
| M 10/21 | Unsupervised Learning: k-Means and Mixtures | Eric | |
| W 10/23 | Unsupervised Learning: Dimensionality Reduction | Eric | Project milestone |
| M 10/28 | Semi-Supervised Learning | William | |
| W 10/30 | Collaborative Filtering and Matrix Factorization | William | Project milestone |
| M 11/4 | Graphical Models 1 | Eric | |
| W 11/6 | Graphical Models 2 | Eric | Project milestone |
| M 11/11 | HMMS, Sequences, and Structured Output Prediction | William | |
| W 11/13 | Topic Models | Eric (William out) | Project milestone |
| 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:
