Resources
Contents []
Python and Jupyter Resources
- Google's Python Class
- Stanford Numpy Tutorial
- numpy for matlab users -great summary table
- Jupyter Notebook Tutorial
- Good intro, including installation, which you don't need. You can skip the lengthy middle section, but note the sample notebooks
- Hands on Machine Learning
- Matplotlib Tutorial Notebook
- Python Data Science Handbook
- Scikit-Learn Documentation
- Google Colab Introduction
Linear Algebra Resources
- Notes from Stanford - short
- 3blue1brown -beautiful animated explanations
- Linear Algebra lectures by Professor Gil Strang at MIT - full course
- A Tutorial on Linear Algebra by Professor C. T. Abdallah
- Linear Algebra Review by Professor Fernando Paganini, UCLA
- The Matrix Cookbook - It won't teach you linear algebra, but this free desktop reference on matrices may come in handy.
Probability Resources
- Notes from Stanford - short
- Review of probability from a course by David Blei at Princeton
- Andrew Moore's Probability tutorial slides (somewhat incomplete)
- Another probability review, from UCI
Textbooks we will draw from
- C. Bishop, Pattern Recognition and Machine Learning. 2007
- our semi-textbook; from an engineering perspective
- Hands on Machine Learning (pdf)
- Has the best Scikit-learn jupyter notebooks
- Dive into Deep Learning
- Has the best pytorch jupyter notebooks
Other textbooks
- LIONbook
- Less technical than what we are doing, but very clearly written
- T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2009
- uses the language and notation of statistics
- K. P. Murphy, Machine Learning: A Probabilistic Perspective 2012
- more depth on probability than we will cover, but good.
- T. Mitchell, Machine Learning. 1997.
- now a bit outdated.
Online Courses
- Coursera ML course - Much more basic than this course, but good intro to ML
- Caltech short ML course - good coverage of several of our topics
Other Resources
- Latex Tutorial; we suggest using Overleaf
- Theoretical CS cheat sheet
- only a subset of this incredible dense packet applies to our course, but it's extremely concise and has seemingly innumerable useful identities.
- Andrew Moore's lecture slides
- we will use a number of these
- ML glossary
- https://detexify.kirelabs.org/classify.html; Here is an awesome website where you can draw symbols and get to know the latex commands for them.