This repository documents my one-year journey to learn more about machine learning. My personal opinion is that learning is best facilitated by reading great books written by experts in a field. This approach guarantees that the source material is of sufficient quality, and avoids the potential pitfall of overemphasizing technology and implementation over ideas.
This repository contains notes and solutions to selected exercises in the following books:
- Pattern Classification by Duda et al. (Notes and solutions), October 2018 to Desember 2018
The material is published here since it might provide value for others. In no way are the notes and solutions meant as a replacement for purchasing the literature. If you decide to look at the solutions, please make an honest attempt at solving the problem on your own first. If you have improvements or additional solutions, please contribute by submitting a Pull Request to this repository.
These are books that I will read to complete this project.
- Pattern Classification by Duda et al.
- Pattern Recognition and Machine Learning by Bishop
- Elements of Statistical Learning by Hastie et al.
These are books that I am currently considering purchasing.
- Machine Learning: A Probabilistic Perspective by Murphy et al.
- Convex Optimization by Boyd et al, there are lectures too.
- Numerical Optimization by Wright et al.
Know of other good books? Let me know!