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:[http://curtis.ml.cmu.edu/w/courses/images/d/de/10601-13F-assignment_2.pdf Naive Bayes & Voted Perceptron] | + | | W 9/11 || [[10-601 Perceptrons and Voted Perceptrons|The Perceptron algorithm]] || William || HW:[http://curtis.ml.cmu.edu/w/courses/images/d/de/10601-13F-assignment_2.pdf Naive Bayes & Voted Perceptron] Download:[http://curtis.ml.cmu.edu/w/courses/images/c/cc/Assignment2-handout.zip data] (due Sept. 13th via Autolab) |
|- | |- | ||
| M 9/16 || The Perceptrons, SVMs, and other Margin Classifiers || William || | | M 9/16 || The Perceptrons, SVMs, and other Margin Classifiers || William || |
Revision as of 21:50, 11 September 2013
This is the syllabus for Machine Learning 10-601 in Fall 2013.
Contents
Prezi Overview of All the Topics in the Course
Schedule
Teaching team: also see the Google Doc Spreadsheet
Date | Topic | Lecturer | Assignment |
---|---|---|---|
M 9/2 | No class - Labor day | ||
W 9/4 | Overview and Intro to Probability | William | HW: worksheet on probabilities (due Sept. 13th via BlackBoard) |
M 9/9 | The Naive Bayes algorithm | William | |
W 9/11 | The Perceptron algorithm | William | HW:Naive Bayes & Voted Perceptron Download:data (due Sept. 13th via Autolab) |
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: TBA |
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 | HW: TBA |
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 | Project milestone |
Th 12/9 | Project due |
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: