Textbook and reference materials
The required textbook for the course is:
For a gentler introduction to machine learning the following text is available for free online:
The following three texts are also excellent and their PDFs are available for free
You may also find these reference materials useful throughout the quarter.
- Machine Learning (and related topics)
- Linear Algebra and Matrix Analysis
- These wonderful videos by 3blue1brown provide a gentle and highly intuitive overview of linear algebra. (The same person created
most of the videos on multivariable calculus on Khan Academy -- also excellent).
- Linear Algebra Review and Reference by Zico Kolter and Chuong Do (free). Light refresher for linear algebra and matrix calculus if you're a bit rusty.
- Linear Algebra, David Cherney, Tom Denton, Rohit Thomas and Andrew Waldron (free). Introductory linear algebra text.
- Matrix Analysis Horn and Johnson. A great reference from elementary to advanced material.
- Probability and Statistics
- Probability Review by Arian Maleki and Tom Do. (From Andrew Ng's machine learning class.)
- Section notes from Anna's 18au offering of 312:
Combinatorics + intro probability,
Random variables & linearity of expectation,
Variance and discrete r.v.s,
Continuous random variables,
CLT, tail bounds and MLE.
- All of Statistics, Larry Wasserman. Chapters 1-5 are a great probability refresher and the book is a good reference for statistics.
- A First Course in Probability, Sheldon Ross. Elementary concepts (previous editions are a couple bucks on Amazon)