Section outline

  • Lecture: What is Machine Learning? Introductory concepts

    Supervised vs. Unsupervised learning. Overview of prediction techniques for supervised learning. Discussion of overfitting, loss functions, parametric vs. non-parametric models, and the bias-variance trade-off.

    Readings:

    • ISLR: Sec 2-2.2.2, Sec 3-3.2
    • ESL: Sec 1

    References for R:

    • https://class.lambdamd.org/pdsr/

    References for basic linear algebra:

    • Session Slides:

    • biostat216 lecture1 File

    • Session Audio/Video Recording (access restricted to registered students):

    • Lecture 1 Media Resource
      Not available unless: You belong to Registered Students Only

    • Homework

    • Homework 1 File
    • Homework 1 turn in Assignment
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    • HW1 soln File
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    • HW1 soln File
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