**Course Description: **Machine learning studies automatic
methods for learning to make accurate predictions or useful decisions
based on past observations and experience, and it has become a highly
successful discipline with applications in many areas such as natural
language processing, computer vision, web mining, or bioinformatics.

This course on the design and theoretical analysis of machine learning
methods will cover a broad range of important problems studied in
theoretical machine learning. It will provide a basic arsenal of
powerful mathematical tools for their analysis, focusing on both
statistical and computational aspects. We will examine questions such
as: What guarantees can we prove on the performance of learning
algorithms? What can we say about the inherent ease or
difficulty of learning problems? Can we devise models that are both amenable
to mathematical analysis and are successful empirically? In addressing these and related
questions we will make connections to statistics, algorithms,
complexity theory, information theory, optimization, game theory, and empirical
machine learning research.

**Instructor:** Maria
Florina Balcan

Office hours: Mon: 4:30 - 5:30, Klaus 2144.

**Teaching Assistant:** Chris Berlind

Office hours: Tue: 3:00 - 4:30, in the common area outside Klaus of 2140.

**Prerequisites: **Either a good Machine Learning or a good
Theory/Algorithms background.

**Evaluation and Responsibilities:** Grading will be based on 4
homework assignments, a take-home final, and a project and class
presentation. We will use two grading schemes -- to determine your final grade we will use whichever
grading scheme is
best for you.

- Grading scheme 1 (Homework oriented):
- Homeworks - 60%
- Take Home Final - 10%
- Project - 30%
- Grading scheme 1 (Project oriented):
- Homeworks - 30%
- Take Home Final - 10%
- Project - 60%

**General structure of the course:** We will use roughly 3/4 of
the
lectures to cover "core" topics in this area, and then will diverge in
the remaining 1/4 based on student interest. Here is a short outline of
the "core" portion (some bullets correspond to multiple lectures):

- Basic models for passive supervised learning: PAC and Statistical Learning Theory
- Simple algorithms and hardness results for passive supervised learning
- Mistake-bound and Online learning. The Weighted-Majority, Winnow, and Perceptron Algorithms
- VC-dimension, uniform convergence; other modern sample complexity results (e.g., Rademacher complexity, localization)
- Margins and support-vector machines; Kernel methods
- Weak-learning vs. Strong-learning; the AdaBoost algorithm
- Modern learning paradigms: Semi-supervised learning, Interactive learning, Distributed Learning, Transfer Learning.

**Textbooks:** The recommended (not required) textbooks are *An
Introduction to Computational Learning Theory * by M. Kearns and
U. Vazirani, and *A Probabilistic
Theory
of Pattern Recognition* by L. Devroye, L. Gy�rfi, G. Lugosi.
Additionally, we will use a number of survery articles and tutorials.