ML Resources
This is a list of useful resources I have stumbled upon while completing my dissertation and learning about machine learning, statistics, and programming.
Courses and Lecture Series'
- CS 47980 - Machine Learning for Intelligent Systems
- Bloomberg - Foundations of Machine Learning
- Computer Science 25300 / 35300 & Statistics 27700 - Mathematical Foundations of Machine Learning
- CS 156 - Learning Systems
- CS 485/685 - Machine Learning Theory: this is a more advanced course that I personally would only encourage taking after taking at least two of the above courses.
Textbooks
- Python Packages: excellent book for writing Python packages.
- Matrix Algebra Useful for Statistics: great resource for learning linear algebra.
- Introducing Python: Modern Computing in Simple Packages: useful for gaining basic knowledge of Python.
- Learning SQL Generate, Manipulate, and Retrieve Data: great resource for gaining SQL knowledge.
- R Packages: great resource for leaning how to make packages in R.
- Advanced R: useful for gaining a deeper understanding of R
- R Inferno: interesting book that delves into the counterintuitive behaviour of R.
- Learning Apache Drill
- Parallel R: useful for setting up parallel computign in R (especially for computationally demanding simulations).
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction: advanced textbook for machine learning.
- Pattern Recognition and Machine Learning: advanced textbook for machine learning.
- Understanding Machine Learning: From Theory to Algorithms: textbook used for CS 485/685
- Applied Predictive Modeling
YouTube Channels
- 3blue1brown: Essentials of Calculus and Essence of Linear Algebra are particularly useful
- Dr. Trevor Bazett: Great channel for more advanced mathematical concepts.
- Ahmad Bazzi: great playlists for Linear Algebra and Convex Optimization
- ritvikmath: great videos that help kick-start initial learning for a variety of machine learning topics.
- Jordan Boyd-Graber: channel has great series for more advanced topics
Blogs
- Richard Harshman: great resource for learning parallel factor analysis
- Gregory Gundersen: contains great posts (I also used it as inspiration for my website)
- Jeremy Kun
- Dr. Mowinckels