You can download the lectures here. We will try to upload lectures prior to their corresponding classes.

  • Chapter 1: Some probability theory
    tl;dr: The very basics of probability theory needed for this lecture.
    [slides] [TexCode]

    Suggested Readings:

  • Chapter 2: Basics of Linear Algebra
    tl;dr: The very basics of linear algebra needed for this lecture.
    [slides] [TexCode]
  • Chapter 3: Multivariate Data Types and Descriptive Statistics
    tl;dr: Multivariate Data Types and Descriptive Statistics.
    [slides] [TexCode] [ggplot examples from the lecture] [ShinyApp (projectoR)]
  • Chapter 4: Multivariate Distributions
    tl;dr: Recap of univariate Random Variables; then definition of random vectors and examples of common distributions.
    [slides] [TexCode]
  • Chapter 5: Distance and Similarity measures
    tl;dr: Introduction to Metric spaces; followed by an overview of common Distance and Similarity measures.
    [slides] [TexCode]
  • Chapter 6: Supervised Learning
    tl;dr: Introduction to Supervised Learning.
    [slides] [TexCode]
  • Chapter 7.1: Unsupervised Learning: Clustering
    tl;dr: Introduction to Unsupervised Learning plus Clustering methods.
    [slides] [TexCode]
  • Chapter 7.2: Dimensionality Reduction: a Motivation
    tl;dr: Motivation for dimensionality reduction methods
    [slides] [TexCode]
  • Chapter 7.3: Principal Component Analysis (PCA)
    tl;dr: An introduction to PCA
    [slides] [TexCode]
  • Chapter 7.4: Multidimensional scaling (MDS)
    tl;dr: An introduction to MDS
    [slides] [TexCode]
  • Chapter 8: Applications and Case Studies in R
    tl;dr: Anecdotal Rapplications.
    [HTML] [printed pdf] [data]