Teaching

Teaching

Courses

  • Learning Sustainable Well-Being: Compassion for Self and Others, a Computing Perspective
    CSE 88
    Spring 2026 Winter 2026
    This experiential course teaches the art of practicing psychological well-being, based on Eastern and Western approaches (e.g., Mindfulness, Positive Psychology, Cognitive Therapy, Neuroscience, Theatre and Art), from a computing perspective.Each week, there is a short lecture on a given topic, combined with workshop-style exercises.
  • Introduction to Artificial Intelligence
    CSE 25
    Winter 2026
    This course provides a first introduction to artificial intelligence (AI). It covers the definition of AI, the history of AI, the main approaches to AI, and example applications of AI and machine learning (ML). Concepts will be grounded in a range of real-world application projects in AI. Students will also be introduced to ethical issues around AI.
  • Principles of Artificial Intelligence: Probabilistic Reasoning and Learning
    CSE 250A
    Fall 2025 Spring 2025 Fall 2024
    Methods based on probability theory for reasoning and learning under uncertainty. Content may include directed and undirected probabilistic graphical models, exact and approximate inference, latent variables, expectation-maximization, hidden Markov models, Markov decision processes, applications to vision, robotics, speech, and/or text.
  • Introduction to Computer Science Research
    CSE 193
    Fall 2025 Fall 2024
    Introduction to research in computer science. Topics include defining a CS research problem, finding and reading technical papers, oral communication, technical writing, and independent learning. Course participants apprentice with a CSE research group and propose an original research project.
  • Introduction to Artificial Intelligence: Probabilistic Reasoning and Decision-Making
    CSE 150A
    Fall 2025 Spring 2025 Fall 2024
    Introduction to probabilistic models at the heart of modern artificial intelligence. Specific topics to be covered include probabilistic methods for reasoning and decision-making under uncertainty; inference and learning in Bayesian networks; prediction and planning in Markov decision processes; applications to intelligent systems, speech and natural language processing, information retrieval, and robotics.