Learning Resources
Curated recommendations for those interested in machine learning, deep learning, and AI research. These resources have shaped my understanding and continue to be valuable references.
Machine Learning Foundations
Elements of Statistical Learning
Written by pioneers in the field. A comprehensive mathematical treatment of statistical learning methods.
Probabilistic Machine Learning
Excellent background into the CS world of machine learning with probabilistic foundations.
Understanding Machine Learning: From Theory to Algorithms
Mathematical approach to understanding the theoretical foundations of machine learning.
Foundations of Machine Learning
Strong mathematical emphasis on the algorithmic and theoretical aspects of ML.
Deep Learning
Neural Networks and Deep Learning
Stellar introduction to neural networks from scratch. Highly accessible and intuitive.
Deep Learning
Comprehensive overview of deep learning practices and theoretical foundations.
The Principles of Deep Learning Theory
In-depth theoretical perspective on the mathematical foundations of deep learning.
Online Courses & Tutorials
Stanford Machine Learning Course
YouTube / Coursera
Classic introduction to machine learning fundamentals and algorithms.
Introduction to Deep Learning
Various platforms
Comprehensive tutorials covering neural network basics to advanced architectures.
Stanford Computer Vision Course
CS231n
Deep learning for computer vision applications and convolutional networks.
Deep Learning at Oxford
University of Oxford
Advanced deep learning course covering modern architectures and techniques.
Prerequisites & Background
For those interested in serious machine learning research, I recommend strong foundations in:
- Linear algebra and matrix theory
- Multivariable calculus and optimization
- Statistics and probability theory
- Information theory basics
- Programming (Python, mathematical computing)
For Prospective Students
If you're interested in working with me or considering research in AI/ML:
- Masters thesis: I may be interested in supervision if your proposed research aligns with my current focus areas
- Bachelor's thesis: Limited availability for second reading, but please inquire
- Preparation: Strong mathematical background is essential for research-level work
- Contact: Feel free to reach out via email to discuss potential collaboration