Introduction to Machine Learning - 10-701/15-781

Slides in Keynote and PDF.


  • 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