Announcements
- • This web site is for CS6140, section #2 only.
- • Prerequisites: CS 5800 or CS 7800 with a minimum grade of C-. I will not enforce these pre-requisites this year. However, note that you are taking the course at your own risk. I will assume the knowledge of algorithms as well as intellectual capacity and work ethic of a student who passed such a course.
- • How to prepare for the course? See here for some guidance.
- • Spring break: 3/4-3/8, no classes.
- • Midterm exam: 2/26 in class.
- • Final exam: 4/19 in class.
- • Mini-project presentations: Tuesday, 4/16 from 4pm-7:10pm in regular classroom.
- • Office hours change (1/25/2019): Tuesdays 3-4pm. Fridays stay 2-3pm
- • Office hours change (3/20/2019): Rashika’s Thursday’s hours change to 4-5:30pm in WVH 166/168.
- • Additional office hours after the final exam: April 23, 5:30pm-7:10pm ☀️
- • No office hours on Friday, 4/26 ☀️
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Last updated: April 21, 2019
Weekly Schedule
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Week 15, April 15
Topics
- • Project presentations (Tuesday at 4pm in our classroom)
- • Final exam (Friday, regular class time at 5:30pm in our classroom)
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Week 14, April 8
Topics
- • Committee machines
- • Support vector machines
- • Review for the final exam
Reading materials
- • Textbook #1 (Bishop): Sparse Kernel Machines (Chapter 7, Section 7.1)
Handouts and code
- • Committee machines code.
- • Support vector machines (SVMs) slides
- • Graphlet slides (not to be covered in the final exam)
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Week 13, April 1
Topics
- • Neural networks
- • Principal component analysis (PCA)
Reading materials
- • Lecture notes (Radivojac & White): neural networks
- • Textbook #1 (Bishop): Neural Networks (Chapter 5, Sections 5.1-5.5)
- • The backpropagation paper from Nature is available here.
- • The RPROP paper is available here.
Handouts and code
- • A few neural network slides
- • Principal component analysis slides.
Homework assignment
- • Assignment #5 available here. Data sets available here. Due on 04/14/2019.
- • Instructions for final project report are available here. Due on 04/20/2019.
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Week 12, March 25
Topics
- • Classification and regression trees
- • K-means, K-means++, speedups for K-means
Reading materials
Handouts and code
- • K-means clustering slides. (credit: Tan et al.)
- • Shantanu Jain’s slides. (mixture of slides from Tan et al. and papers)
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Week 11, March 18
Topics
- • Classification and regression trees
Reading materials
- • Lecture notes (Radivojac & White): evaluation (incomplete)
Handouts and code
- • Classification trees slides. (credit: Tan et al.)
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Week 10, March 11
Topics
- • Data preprocessing
- • Performance evaluation
Handouts and code
- • Evaluation slides. (credit: Tom Dietterich)
Class scores
- • Analysis of class performance: slides.
Homework assignment
- • Assignment #4 available here. Data set available here. Due on 03/30/2019.
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Week 9, March 4
Spring Break
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Week 8, February 25
Midterm Exam, Tuesday in class.
Topics
Handouts and code
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Week 7, February 18
Topics
- • Perceptron
- • Naive Bayes prediction
- • Review for Midterm Exam
Reading materials
Handouts and code
- • Perceptron MATLAB code.
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Week 6, February 11
Topics
- • No class on Tuesday (snow storm)
- • Finishing logistic regression
Reading materials
- • Lecture notes (Radivojac & White): linear classification (last update on 02/20/2019)
- • Textbook #1 (Bishop): Linear models for classification (Chapter 4)
Handouts and code
- • Logistic regression MATLAB code
Homework assignment
- • Mini project proposal (Assignment #3) available here. Due on 03/03/2019 (new due date 03/06).
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Week 5, February 4
Topics
- • Regularization
- • Generalized linear models
- • Logistic regression
Reading materials
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Week 4, January 28
Topics
Reading materials
Handouts and code
Homework assignment
- • Assignment #2 available here. Due on 02/17/2019.
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Week 3, January 21
Topics
- • Principles of optimization: gradient descent, Lagrange multipliers
- • Introduction to prediction problems
Reading materials
- • Lecture notes (Radivojac & White): optimization
- • Textbook #1 (Bishop): Lagrange multipliers (Appendix E)
- • Lecture notes (Radivojac & White): prediction
Handouts and code
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Week 2, January 14
Topics
- • Basics of parameter estimation
Reading materials
- • Lecture notes (Radivojac & White): estimation
- • Textbook #1 (Bishop): Mixture Models and EM (Chapter 9)
Handouts and code
- • Introduction to parameter estimation slides. (last updates on 01/22/2019)
- • EM algorithm MATLAB code.
Homework assignment
- • Assignment #1 available here. Due on 02/01/2019.
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Week 1, January 7
Topics
- • Class overview and logistics
- • Introduction to machine learning
- • Introduction to probability theory
Reading materials
- • Lecture notes (Radivojac & White): probability (minor: last updates on 01/29/2019)
- • Textbook #1 (Bishop): Introduction (Chapter 1)
Handouts and code
- • Class overview slides.
- • Introduction to probability theory slides (last updates on 1/13/2019).
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