Academics
Browse programs across undergraduate, graduate, doctoral, certificates, and continuing education. Each program page includes outcomes, curriculum structure, teaching format, projects, admissions requirements, tuition, and FAQ.
- BSc
- BA
- MSc
- MA
- PhD
- Certificate
- Continuing Education
Start with your goal (career change / depth / research), then pick field and mode.
- On-campus
- Hybrid
- Low-Residency
On-campus provides full access to facilities; Hybrid blends campus and structured off-campus components; Low-residency concentrates studio weeks and defenses.
- Arts & Humanities
- Business
- Computer Science
- Design
- Education
- Health
- Law & Policy
- Social Sciences
- Sustainability
Fields are not walls. Cross-field electives are encouraged—but core methods must be strong.
Strong foundations in algorithms, data structures, and systems
ML engineering paired with ethics, policy, and product design
Typography-led interface design
How learning works here
Courses are built around repeatable practice: students keep lab logs, iterate on drafts, and defend decisions. Many assignments are designed to become portfolio artifacts.
- Feedback cycles: short, frequent revisions instead of high-stakes surprises.
- Methods-first: we teach protocols and evaluation, not just tools.
- Research adjacency: motivated students can upgrade course work into lab contributions.
If you’re deciding between programs, look for alignment in pacing, mode, and the kind of work you want to ship.
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.
Strong foundations in algorithms, data structures, and systems · Campus-based seminars and supervised labs
Systems thinking for climate and cities · Data literacy for sustainability decisions
Statistics and decision-making in real contexts · Transparent models and communication
European institutions, civic history, and contemporary debates · Policy writing studios and interview-based research
Media theory paired with platform analysis · Digital methods (archives, interviews, data-driven storytelling)
Close reading seminars with small cohorts · Writing-intensive curriculum with feedback cycles
ML engineering paired with ethics, policy, and product design · Evaluation-first approach (bias, robustness, monitoring)
Typography-led interface design · Design systems and component thinking
Evidence-based teaching for diverse cohorts · Learning analytics with a human lens
Security as engineering, not theatre · Applied cryptography and secure systems
Urban systems and climate adaptation · Studio charrette with real city constraints
Data governance, privacy, and clinical workflows · Interoperability and quality improvement
Policy analysis paired with narrative and rhetoric · Studio briefs with stakeholder feedback
Craft seminars paired with an editorial studio · Typography foundations for writers and editors
Co-mentorship across disciplines · Methods spine (qualitative and computational)
Research questions, sources, and argument structure · Citation practice and academic integrity
A structured product case portfolio · Cross-functional communication training
Weekly peer workshop with editor feedback · Learn revision as a deliberate craft