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Level 2 · M.Sc

Advanced AI

MSc 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

1. Phase 0 From builder to architect
2. Phase 1 Transformer & attention (architect-depth recap)
3. Phase 2 Modern LLM landscape
4. Phase 3 Decoding & sampling
5. Phase 4 Tokenization & context windows
6. Phase 5 Embeddings & vector spaces
7. Phase 6 Evaluation foundations
8. Phase 7 Prompt anatomy & instruction design
9. Phase 8 Reasoning I: chain-of-thought, decomposition, least-to-most
10. Phase 9 Reasoning II: self-consistency, reflection & debate
11. Phase 10 Tool-augmented reasoning: ReAct & plan-execute
12. Phase 11 Structured & constrained outputs
13. Phase 12 Hallucination I: taxonomy, root causes, when models fabricate
14. Phase 13 Hallucination II: mitigation
15. Phase 14 Prompt optimization
16. Phase 15 Prompt security
17. Phase 16 Context engineering
18. Phase 17 RAG fundamentals
19. Phase 18 Advanced retrieval
20. Phase 19 Advanced RAG: multi-hop, graph RAG, agentic retrieval
21. Phase 20 RAG evaluation
22. Phase 21 Memory systems
23. Phase 22 Feature context & context-as-a-product
24. Phase 23 Knowledge management: corpora, freshness, provenance, governance
25. Phase 24 Tool design: interfaces, affordances, error handling, safety
26. Phase 25 The agent loop: plan–act–observe; single-agent patterns
27. Phase 26 Specialized agents & roles; agent design patterns
28. Phase 27 Multi-agent orchestration I: supervisor/worker, hand-offs
29. Phase 28 Multi-agent orchestration II: parallel fan-out, deterministic workflows, pipelines
30. Phase 29 Skills & modularized AI: reusable capabilities, composition
31. Phase 30 Agent memory, state & long-running/durable tasks
32. Phase 31 Agent reliability: guardrails, validation, human-in-the-loop, recovery
33. Phase 32 Agent evaluation, observability, tracing & debugging
34. Phase 33 Architecture principles: modularization & separation of concerns
35. Phase 34 Blueprints: designing & documenting AI systems for big/complex projects
36. Phase 35 Decomposition & context boundaries; interfaces between AI modules
37. Phase 36 Architectural patterns: router, pipeline, cascade, ensemble, blackboard, fallback
38. Phase 37 Reliability & resilience: retries, circuit breakers, graceful degradation
39. Phase 38 Cost/latency/quality optimization: model routing, caching, distillation choices
40. Phase 39 Security & privacy architecture: PII, secrets, tenant isolation, system-level injection defense
41. Phase 40 Scaling complex AI projects: organizing many agents, skills & contexts; system governance
42. Phase 41 Data strategy for applied AI; pipelines; synthetic data