a hand on guide in machine learning, designed to take you from mathematical foundations to cutting-edge research. designed to take you from middle school to PhD. each topic includes:
- theoretical foundations with rigorous mathematical treatment
- hands-on implementation from scratch
- research connections to current literature
- socratic questions to deepen understanding
- curated resources from top institutions
build rock-solid mathematical foundations and master classical machine learning.
quarter 1-2: mathematical foundations
- real & functional analysis
- linear algebra & matrix theory
- probability theory & stochastic processes
- optimization theory
quarter 3-4: statistical learning & classical ML
- PAC learning & VC theory
- kernel methods
- tree-based methods
- bayesian inference
master neural networks from theory to practice.
quarter 1: neural network theory
- approximation theory
- backpropagation calculus
- initialization & normalization
quarter 2: architectures
- CNNs, RNNs, transformers
- graph neural networks
- attention mechanisms
quarter 3: optimization
- first & second-order methods
- variance reduction
- distributed optimization
quarter 4: research project
- reproduce 3 seminal papers
- original contribution
deep dive into advanced topics.
quarter 1: generative models
- VAEs, GANs, normalizing flows
- diffusion models & score matching
- energy-based models
- flow matching
quarter 2: reinforcement learning
- tabular methods to deep RL
- model-based RL
- multi-agent systems
- offline RL
quarter 3: specialization track choose your research focus area
quarter 4: research
- conference paper submission
push the boundaries of ML.
quarter 1-2: advanced topics
- large language models
- multimodal learning
- AI alignment & safety
- mechanistic interpretability
quarter 3-4: dissertation
- original research contributions
- multiple paper submissions
- first principles: derive everything from mathematical foundations
- implementation first: code before using libraries
- theory ↔ practice: connect mathematical insights to empirical results
- research mindset: question assumptions, propose extensions
- follow the order: topics build on each other systematically
- complete exercises: implementation is crucial for understanding
- read papers: each lesson connects to research literature
- test understanding: answer socratic questions before moving on
- build projects: apply knowledge to increasingly complex problems
- basic python programming
- high school mathematics
- dedication to deep understanding
begin with 00-mathematical-foundations/01-analysis/01-real-analysis/ and work through systematically. each directory contains:
lesson.md: theory and conceptsexercise.py: implementation templatetest_implementation.py: verify your solutionsolutions/: reference implementations (try solo first!)
- statistical learning theory
- optimization (convex & non-convex)
- deep learning architectures
- generative models (VAEs, GANs, diffusion, flows)
- reinforcement learning
- natural language processing
- computer vision
- neural ODEs/SDEs
- geometric deep learning
- meta-learning
- continual learning
- causal representation learning
- quantum machine learning
- energy-based models
- flow matching
- mechanistic interpretability
- approximation theory
- optimization landscape analysis
- generalization theory
- information geometry
- optimal transport
each lesson includes:
- original papers
- lecture notes from stanford, MIT, berkeley
- video lectures
- implementation tutorials
- advanced reading for deeper dives
this curriculum is a living document. contributions welcome for:
- additional exercises
- clearer explanations
- new research connections
- bug fixes in implementations
MIT license - learn freely, build amazing things!