Course Catalog

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.

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.

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.

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.

Learn computational thinking as a transferable method: decomposition, abstraction, representation, and systematic debugging. Students solve small problems with simple programs and visual reasoning, practicing how to explain solutions and verify them with tests and examples.

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.

Explore operating-system fundamentals: processes/threads, scheduling, memory management, files, and concurrency primitives. Students build disciplined debugging habits and reason about race conditions, deadlocks, and performance at the systems boundary.

Develop a practical testing and reliability toolkit: unit/integration strategies, property-based thinking, observability signals, and incident response. Students write tests that prevent regressions, design for failure, and produce postmortems that improve systems over time.

A mentored capstone studio culminating in an external-style review. Students propose, build, and ship a substantial artifact with documentation, evaluation, and an oral defense; emphasis on scope control, iteration, and evidence of impact.