index – Alex Smola

Practical Information

• Lectures: Monday and Wednesday, 10:30AM to 11:50AM
• Location: Wean Hall 7500
• Recitations: Tuesdays 5–6:30pm
• Location: Wean Hall 7500
• Instructor: Geoffrey J. Gordon and Alex Smola
• TAs: Carlton Downey, Ahmed Hefny, Dougal Sutherland, Leila Wehbe, and Jing Xiang
• Grading Policy: Homework (33%), Midterm (33%), Project (34%)

Office Hours

Overview

Machine learning studies the question “how can we build computer programs that automatically improve their performance through experience?” This includes learning to perform many types of tasks based on many types of experience. For example, it includes robots learning to better navigate based on experience gained by roaming their environments, medical decision aids that learn to predict which therapies work best for which diseases based on data mining of historical health records, and speech recognition systems that learn to better understand your speech based on experience listening to you.

This course is designed to give PhD students a thorough grounding in the methods, theory, mathematics and algorithms needed to do research and applications in machine learning. The topics of the course draw from machine learning, classical statistics, data mining, Bayesian statistics and information theory. Students entering the class with a pre-existing working knowledge of probability, statistics and algorithms will be at an advantage, but the class has been designed so that anyone with a strong numerate background can catch up and fully participate.

Resources

For specific videos of the class, as well as slides, go to the individual lectures in the schedule below or the menu at left. This is also where you will find pointers to further reading material etc.

• Problem sets 1 2 3 4
• Solutions HW1.Q1 HW1.Q3 HW2.Q1-2 HW3.Q1 HW3.Q2 HW4.Q1-2

Prerequisites

• Basic probability and statistics are a plus.
• Basic linear algebra (matrices, vectors, eigenvalues) is a plus. Knowing functional analysis would be great but not required.
• Ability to write code that exceeds “Hello World.” Preferably beyond Matlab or R.
• Basic knowledge of optimization. Having attended a convex optimization class would be great but the recitations will cover this.
• You should have no trouble answering the questions of the self evaluation handed out for the 10-601 course.

Course Pages

• Google Group
• HW submission
• Project
• Extra credit

Schedule

Units

1. Introduction
2. Basic Tools
3. Naive Bayes
4. Perceptron
5. Optimization
6. Duality
7. SVM
8. Kernel Methods
9. Tail Bounds, Averages
10. Bootstrap
11. Graphical Models

Recitations

1. Linear Algebra
2. Probability
3. Kernels
4. Optimization
5. Duality & SVM
6. Tail Bounds & Averages
7. Learning Theory
8. Information Theory
9. Graphical Models

Team

• Geoff Gordon
• Alex Smola
• Carlton Downey
• Ahmed Hefny
• Dougal Sutherland
• Leila Wehbe
• Jing Xiang
• MLD | CMU