Announcements
- • 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.
- • Midterm exam: October 22, in class.
- • Final exam: 12/06, in class.
- • Academic calendar: available here.
- • Office hours after the final exam: 12/13, 11am-6:30pm. ☀️
———————————————————————
Last updated: December 4, 2019
Weekly Schedule
———————————————————————
Week 14, December 2 ☀️
Final exam, Friday
Topics
- • Support vector machines
Reading materials
- • Textbook #1 (Bishop): Sparse Kernel Machines (Chapter 7, Section 7.1)
Handouts and code
- • Support vector machine slides
———————————————————————
Week 13, November 25
Topics
- • Support vector machines
- • Committee machines
Reading materials
- • Textbook #1 (Bishop): Combining models (Chapter 14, Sections 14.1-14.4)
- • Textbook #1 (Bishop): Sparse Kernel Machines (Chapter 7, Section 7.1)
Handouts and code
———————————————————————
Week 12, November 18
Topics
- • Neural networks
- • Committee machines
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 (updated on 12/4) ☀️
- • Neural network and committee machine code.
———————————————————————
Week 11, November 11
Topics
- • Data preprocessing
- • Learning from positive-unlabeled data
Handouts and code
- • Positive-unlabeled learning slides
Homework assignment
- • Assignment #4 available here.
- • Mini project report instructions, available here.
———————————————————————
Week 10, November 4
Topics
- • Model evaluation
- • Data preprocessing
Handouts and code
- • Tom Dietterich’s evaluation slides.
- • Data preprocessing slides.
- • Performance scores for class slides. ☀️
———————————————————————
Week 9, October 28
Topics
- • Review of the midterm exam
- • Logistic regression
———————————————————————
Week 8, October 21
Midterm exam, Tuesday
Topics
Handouts and code
- • Logistic regression slides.
- • Logistic regression code.
———————————————————————
Week 7, October 14
Topics
- • K-means clustering
- • Classification trees
Reading materials
Handouts and code
———————————————————————
Week 6, October 7
Topics
- • Regularization
- • Newton-Raphson optimization
- • Perceptron
Reading materials
Handouts and code
- • Optimization slides.
- • Linear classification slides.
- • Perceptron MATLAB code here.
———————————————————————
Week 5, September 30
Topics
- • Naive Bayes classification and regression
- • Linear regression
- • Radial basis function networks
Reading materials
Handouts and code
- • Lecture slides.
- • Ordinary least squares (OLS) regression MATLAB code here.
Homework assignment
- • Assignment #2 available here.
- • Assignment #3, project proposal instructions, available here.
———————————————————————
Week 4, September 23
Topics
- • Introduction to prediction problems
- • Optimal classification and regression
- • Bias-variance tradeoff
Reading materials
Handouts and code
- • Introduction to prediction problems slides. (updated 09/28/2019)
———————————————————————
Week 3, September 16
Topics
- • Parameter estimation for mixture models
- • Expectation-maximization (EM) algorithm
Handouts and code
- • EM algorithm MATLAB code.
———————————————————————
Week 2, September 9
Topics
- • Introduction to probability theory
- • 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.
Homework assignment
- • Assignment #1 available here.
———————————————————————
Week 1, September 2
Topics
- • Class overview and logistics
- • Introduction to machine learning
- • Introduction to probability theory
Reading materials
- • Lecture notes (Radivojac & White): probability
- • Textbook #1 (Bishop): Introduction (Chapter 1)
Handouts and code
- • Class overview slides.
- • Introduction to probability theory slides.
———————————————————————
———————————————————————