EthanHellman

Core course on supervised learning, unsupervised learning, and probabilistic inference with applications in NLP, vision, and bioinformatics.

Comprehensive overview of deep neural networks including convolutional, recurrent, and generative models, with hands-on TensorFlow implementation.

Project-based course focused on applying classical and modern ML techniques to real-world problems across multiple domains.

Few-shot, meta-, and transfer learning methods including MAML and optimization-based adaptation techniques.

Deep NLP architectures like transformers, RNNs, attention mechanisms, and contextual embeddings.

Policy gradients, actor-critic methods, and multi-agent systems for real-world RL applications.

Lifelong learning, curriculum learning, and agents that autonomously improve over time with self-generated feedback.

Fundamentals of search, logic, probabilistic reasoning, MDPs, and decision-making under uncertainty.

Large language models and machine learning systems that automate software engineering tasks like synthesis, repair, and optimization.

Interdisciplinary course blending neuroscience, cognitive science, and AI to model intelligence in humans and machines.

Focuses on responsible AI system design, including fairness, interpretability, user trust, and societal impact.

Examines how AI is reshaping economic, societal, and global institutions, with emphasis on policy and governance.

Fundamentals of interaction design, prototyping, user testing, and interface development.

Graduate research seminar on advanced topics in HCI including cognition, accessibility, XR, and design theory.

Studio-based design course focused on prototyping and testing AI-infused experiences and intelligent interfaces.

Behavioral design techniques to build products that nudge users toward better habits and decisions.

Computational study of social systems, including crowdsourcing, trust, online collaboration, and community design.

Ongoing speaker series featuring cutting-edge HCI research from academia and industry.

Algorithmic problem-solving using dynamic programming, greedy algorithms, divide-and-conquer, and complexity theory.

Formal logic, SAT solving, and symbolic reasoning for AI and formal verification.

Proof techniques, discrete math, computability, set theory, and Turing machines.

C/C++ systems programming, multithreading, synchronization, memory management, and system abstractions.

Low-level systems architecture, pointer arithmetic, memory layout, and assembly/C programming.

Frontend and backend engineering including REST APIs, databases, auth, security, and performance.

SQL, relational algebra, normalization, indexing, transactions, and NoSQL systems.

Consensus protocols, smart contracts, and decentralized application development from a systems/security perspective.

Techniques for deploying deep learning models on low-power edge devices through quantization and model compression.

Research under Andrew Ng focusing on real-world machine learning systems and model development.

Bridges engineering with product strategy, user needs, and go-to-market execution.

Lean startup methodology applied to national security and defense innovation challenges.

Examines government–tech industry collaboration, procurement, and policy innovation.

Explores ethical frameworks in AI, automation, algorithmic bias, and data privacy.