Introduction

Slides in Keynote and PDF.

Syllabus

• Administrative details

• Programming with data

  • Overview of machine learning problems

  • Supervised problems (regression, classification, sequence annotation)

  • Unsupervised problems (clustering, topics, subspaces)

• Problem Settings

  • Nonresponsive environment (induction, transduction, covariate shift)

  • Responsive environment (batch, online, active learning, bandits, reinforcement learning)

  • Discriminative vs. generative models

• Data

  • Internet (traffic, user generated content, activity)

  • Medicine (hospitals, healthcare, sequencing)

  • Physics

• Basic tools

  • Nearest neighbors

  • Linear regression

Background Material

• Introduction to Machine Learning handout

• UCI Machine Learning Repository. This is the original repository for a very diverse set of papers.

• Machine Learning Summer School 2014 in Pittsburgh. Many overview courses, tutorials, slides, etc.

• ImageNet dataset. This is what many researchers use for computer vision.

• MNIST dataset of handwritten digits.

Videos