Learning Machines

Taught by Patrick Hebron at ITP, Fall 2016
Previous Edition: Fall 2015

Overview:

This course aims to introduce machine learning, a complex and quickly evolving subject. In the first half of the semester, we will investigate the conceptual and technical workings of a few key machine learning models, their underlying mathematics and their philosophical value in understanding the general phenomena of learning and experience. In the second half of the semester, we will focus on the development of software projects that apply machine learning to real-world problems.

Required Text:

Syllabus Overview:

Week 1:

Week 2:

Week 3:

Week 4:

Week 5:

Week 6:

Week 7:

Week 8:

Week 9:

Week 10:

Week 11:

Week 12:

A Note About 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.

Additional Resources:

Python Installation Resources:

Python Resources:

Math for Machine Learning:

Academic Research Tools:

Going Further: