Lecture 1. Supervised Learning 1

Learning Goals: You should know the concepts and basic mathematics of Linear Regression, Least squares optimization, overfitting/underfitting, the bias-variance tradeoff, cross-validation, regularization, k-nearest neighbor regression/classification. You should also be able to run and make modifications to basic matlab and python (scikit-learn) code for such algorithms.

IntroSlides2024 Download IntroSlides2024

Lecture01slides.pdf Download Lecture01slides.pdf (2023 version - includes correction of one or two typos)

Lecture01small.pdf Download Lecture01small.pdf

Available via youtube: https://youtu.be/JF6BugAf0wA Links to an external site.

and with subtitles via Canvas Studio here

Reading Assignment (this refers to the book Machine Learning (Links to an external site.) by Lindholm et al):

  • Ch 2.1 and 2.2
  • Ch 3.1 and  3.3
  • Ch 4.1-4.4
  • Ch 5.1 and 5.3

Videos related to the lecture:

Code used in the lecture (please don't worry about the unlogical naming of some of these files...)