Difference between revisions of "Syllabus for Machine Learning 10-601 in Fall 2013"
From Cohen Courses
Jump to navigationJump to searchLine 23: | Line 23: | ||
|- | |- | ||
| W 9/11 || [[10-601 Perceptrons and Voted Perceptrons|The Perceptron algorithm]] || William || HW2:[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. 18th via Autolab) | | W 9/11 || [[10-601 Perceptrons and Voted Perceptrons|The Perceptron algorithm]] || William || HW2:[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. 18th via Autolab) | ||
− | |||
|- | |- | ||
| M 9/16 || [[10-601 Logistic Regression|Logistic Regression]] || William || | | M 9/16 || [[10-601 Logistic Regression|Logistic Regression]] || William || | ||
|- | |- | ||
− | | W 9/18 || [[10-601 SVMS|SVMs and Margin Classifiers]] || William || HW3: [http://curtis.ml.cmu.edu/w/courses/images/c/ce/10601-13F-assignment_3.pdf Logistic Regression] Download: [http://curtis.ml.cmu.edu/w/courses/images/3/3a/Handout.mat data] (due Sept. 25th via Autolab) | + | | W 9/18 || [[10-601 SVMS|SVMs and Margin Classifiers]] || William || HW3: [http://curtis.ml.cmu.edu/w/courses/images/c/ce/10601-13F-assignment_3.pdf Logistic Regression] Download: [http://curtis.ml.cmu.edu/w/courses/images/3/3a/Handout.mat data] (due Sept. 25th via Autolab) [http://curtis.ml.cmu.edu/w/courses/images/d/d4/Hw3_solution_example.zip example solution] |
|- | |- | ||
| M 9/23 || [[10-601 Linear Regression|Linear Regression]] || Eric | | M 9/23 || [[10-601 Linear Regression|Linear Regression]] || Eric |
Revision as of 16:57, 2 October 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 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 Download:data (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: Experimentation: Compare classifiers |
M 10/7 | PAC Learning | Eric (William out) | |
W 10/9 | Bias-Variance Decomposition | Eric (William out) | HW6: 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: Worksheet on Graphical Models |
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: