MSc Human-Centered AI

Covers supervised learning end-to-end: baselines, feature engineering, train/validation discipline, and error analysis. Students practice building reproducible evaluation reports, spotting leakage/overfitting, and communicating results with clear metrics and caveats.

Build production-grade LLM applications beyond prompting: retrieval and grounding, safety and policy checks, tool use, and systematic evaluation harnesses. Students implement test suites for quality, hallucination risk, and regression, then iterate on architecture with measured evidence.

Design and operate responsible ML services: evaluation protocols, bias and slice checks, monitoring/alerts, rollback strategies, and incident-style postmortems. Emphasis on documentation, governance, and shipping models that remain reliable under drift.

An introduction to programming through small, complete systems: variables, control flow, functions, and working with files. Strong emphasis on readable code, incremental testing, debugging via tracing, and using version control to iterate with feedback.

Study core data structures and complexity with hands-on implementation: stacks, queues, trees, hash tables, and graphs. Focus on choosing the right structure, analyzing time/space tradeoffs, and writing performance-aware code with clear invariants.

Model real domains in relational databases: schema design, normalization, SQL querying, indexing, and integrity constraints. Includes a mini project with audits and migrations, emphasizing data quality, reproducibility, and reasoning about consistency.