Learning Machines

Taught by Patrick Hebron at ITP, Fall 2015


Overview:


This half-semester course aims to introduce machine learning, a complex and quickly evolving subject deserving of a far more intensive study. The goal of this course will be to open a preliminary investigation of the conceptual and technical workings of a few key machine learning models, their underlying mathematics, their application to real-world problems and their philosophical value in understanding the general phenomena of learning and experience.

Primary Sources:


Before an advancement in machine learning is distilled into textbooks, tutorials, blogs and open-source implementations, it is generally introduced in the form of an academic research paper. Many of these papers can be found at Arxiv and the other sites listed in the Academic Research Tools section below. These documents are not easy to read - they often describe ideas using mathematical nomenclature and assume that the reader is already familiar with the subject. Yet, these research papers are the best way to access the current cutting edge within machine learning. For this reason, it is important to become familiar with the format and decyphering its contents. To aid this process, we will read and discuss a primary source research paper each week. The primary source readings are labeled as such in the syllabus below.

Resources:


Required Text:

Python Installation Resources:

Python Resources:

Math for Machine Learning:

Academic Research Tools:

Going Further:

Syllabus:


Week 1:

Class:

Homework:

Assignment:

Readings:

Optional:

Week 2:

Class:

Homework:

Assignment:

Readings:

Week 3:

Class:

Homework:

Assignment:

Readings:

Week 4:

Class:

Homework:

Assignment:

Readings:

Optional:

Week 5:

Class:

Homework:

Assignment:

Readings:

Optional:

Week 6:

Class:

Homework:

Assignment:

Readings:

Week 7:

Class: