Welcome to my machine learning repository! This space is dedicated to sharing my journey and the various resources that have been instrumental in my learning process. Below, you'll find a brief overview of the key components of this repository.
One key realization I've had in my studies is the importance of understanding probability and linear algebra. These foundational topics make machine learning much more approachable and allow for a deeper comprehension of the algorithms and methodologies involved. Here is a list of courses I've taken at university. These classes have provided a strong foundation in the theoretical and practical aspects of machine learning, and some of the projects and assignments from these courses are included in this repository. The classes are:
- MATH 19620: Linear Algebra
- STAT 23400: Statistical Models and Methods
- CMSC 25300: The Mathematical Foundations of Machine Learning
- CMSC 25400: Machine Learning
Throughout my learning, I've found the following textbooks to be incredibly helpful. They offer in-depth insights and explanations on various topics related to machine learning. Here are my top picks:
- Probabilistic Machine Learning: An Introduction by Kevin Patrick Murphy, MIT Press, 2021.
- Mathematics for Machine Learning by Marc Peter Deisenroth, A Aldo Faisal, and Cheng Soon Ong, Cambridge University Press, 2020.
- Pattern Recognition and Machine Learning by Christopher Bishop, 2006.
- The Elements of Statistical Learning (second edition) by Trevor Hastie, Robert Tibshirani, Jerome Friedman, 2009.
In this repository, you'll find a variety of mini-projects, and over time, other materials that I've compiled and created throughout my learning journey.
Happy learning!