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Level 2 · M.Sc

MSc Advanced AI

Artificial Intelligence · Live · v0

Doctoral AI — advanced architectures, training at scale, evaluation science, and an original verified contribution.

Path type

Artificial Intelligence

Requirements

MSc Applied AI Systems

Enrollment

€19.95

Deadline

Next cohort starts 2027-09-06 (AY 2027/28)

Length

~1 year · 42 phases

Language

English · Español

Content freshness

Curriculum updated 2026-08-22 · content rev 4b78e107

Cortex Credits (CC)

126 CC · 421 CC across all tiers of this programme
What are Cortex Credits?

Syllabus

View the phase syllabus

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

Syllabus →

Enrolment prerequisites

• A verified account and admissions-committee approval.
• Recommended path: MSc Applied AI Systems (advisory, not blocking).