Level 2 · M.Sc

MSc Applied AI Systems

Syllabus · 42 phases · ~1 year

Every unit this programme teaches, in the order it is taught. Headlines only — the material itself opens once you are enrolled.

Dates, price and prerequisites

Phase 0 Foundations
Phase 1 Hardware & Computing Substrate
Phase 2 Numerical Representation
Phase 3 Linear Algebra from First Principles
Phase 4 Calculus & Optimization for AI
Phase 5 Probability & Information Theory
Phase 6 Python for AI Engineering
Phase 7 Scalar Autograd from Scratch (minigrad.scalar)
Phase 8 Tensor Autograd from Scratch
Phase 9 MLP, Modules, and Optimizers
Phase 10 Initialization, Normalization, Residuals
Phase 11 Tokenization Theory + BPE Implementation
Phase 12 The Corpus: Designing the Microscopic Dataset
Phase 13 Embeddings & Representation Spaces
Phase 14 Pre-Transformer Sequence Models
Phase 15 Attention from Scratch
Phase 16 Positional Encodings
Phase 17 Tiny Transformer Block & Mini-GPT
Phase 18 Training Loop, Checkpointing, Mixed-Precision Preview
Phase 19 Training Dynamics & Debugging
Phase 20 Evaluation Harness
Phase 21 Inference Internals & Sampling
Phase 22 KV Cache: From Math to Memory
Phase 23 GPU Architecture Fundamentals
Phase 24 CUDA & Triton Hands-On
Phase 25 PyTorch Internals
Phase 26 Quantization Deep Dive
Phase 27 Modern Attention Optimizations
Phase 28 Fine-Tuning, LoRA, QLoRA
Phase 29 Retrieval-Augmented Generation (RAG)
Phase 30 Structured Generation & Constrained Decoding
Phase 31 Tool Use & the Model Context Protocol (MCP)
Phase 32 Agents: Planning, Memory, Sandboxing (Capstone Application)
Phase 33 Inference Serving: From FastAPI to Continuous Batching
Phase 34 Observability, Cost & Capacity
Phase 35 Distributed Training & Inference
Phase 36 Frontier Architectures
Phase 37 Security & Safety of AI Systems
Phase 38 MLOps
Phase 39 Capstone: The Miniature Production System
Phase 40 Hardening, Postmortem, "What's Next"
Phase 41 Learner Portal: Delivering the Curriculum to Many