Welcome to the Virtum's homepage!
My homepage for ai-related materials
Roadmap for beginners
Theory
Learning materials
Online courses
- Coursera, Machine learning specialization, University of Washington This the most basic course. It covers all the basics and has a few programming tasks. Math knowledge needed is covered in the course materials. Technology: Jupyter / IPython.
- Coursera, Andrew Ng, Machine learining, Stanford University This course is recommended for all beginners. It skims over math except for matrix operations which are covered in the course materials. There are some programming assignments, but rather an afterthought. Technology: Octave, If you want to go deeper, try Andrew Ng’s Stanford lectures
- Coursera, Neural networks for machine learning, University of Toronto This is a tough one. One needs to understand partial derivatives quite well and algebra (dot product, etc. It is not covered in the course) to effectively follow it. Programming part is really tiny and based on Octave. You probably need pen&paper rather than your computer.
- Coursera, Probabilistic Graphical Models, Stanford University I recommend doing this course before the neural networks one as this clarifies Bayes models which are used all over the place in the second part o NN. Navie Bayes is covered as well.
- Coursera, Deep-Learning
Lectures
Practice
Data sets for training and experimentation
Software aids
Other