Textbook and reference materials

The required textbook for the course is:

• [Murphy] Probabilistic Machine Learning: An Introduction, Kevin Murphy, 2022 (Note, this is not the 2012 edition used in past years). Follow the link to obtain a free, PDF pre-print as well as options to purchase a hard copy.

The following three texts are also excellent and their PDFs are available for free online.

• [B] Pattern Recognition and Machine Learning, Christopher Bishop.
• [HTF] The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Trevor Hastie, Robert Tibshirani, Jerome Friedman.
• [EH] Computer Age Statistical Inference: Algorithms, Evidence and Data Science, Bradley Efron, Trevor Hastie.

You may also find these reference materials useful throughout the quarter.

• Machine Learning (and related topics)
• Understanding Machine Learning: From Theory to Algorithms, Shai Shalev-Shwartz, Shai Ben-David. An introduction to theoretical machine learning.
• Foundations of Data Science, by Avrim Blum, John Hopcroft and Ravi Kannan. This freely available pdf has nice chapters on machine learning (chapter 5), clustering (chapter 7) and SVD (chapter 3).
• 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
• 20su offering of CSE312 materials and video lectures by Alex Tsun. Slides, full notes, and short video lecture snippets on basic probabilty and statistics. This course was recently redesigned in part to better prepare students for CSE 446, making it an excellent resource for review.
• Probability Review by Arian Maleki and Tom Do. (From Andrew Ng's machine learning class.)
• Section notes from Anna Karlin's 18au offering of 312:
  Counting, Combinatorics + intro probability, Conditional probability; Random variables & linearity of expectation, Variance and discrete r.v.s, Conditional expectation, Joint distributions, 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)
• Optimization
• Numerical Optimization, Nocedal, Wright. Practical algorithms and advice for general optimization problems.
• Convex Optimization: Algorithms and Complexity, Sébastien Bubeck. Elegant proofs for the most popular optimization procedures used in machine learning.
• Python
• www.learnpython.org "Whether you are an experienced programmer or not, this website is intended for everyone who wishes to learn the Python programming language."
• NumPy for Matlab users
• Latex
• Learn Latex in 30 minutes
• Overleaf. An online Latex editor.
• Standalone Latex editor on your local machine
• Latex Math symbols
• Detexify LaTeX handwritten symbol recognition

---