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Courses

Curated external courses I recommend, with brief notes and links.

University Courses

  • CMU Advanced Natural Language Processing (Fall 2025)
    Course site · YouTube playlist
    Graduate-level, practice-oriented coverage of modern NLP: transformers and LLMs, prompting and agents, alignment (RL/GRPO), evaluation, safety, and applied projects.

  • Lecture: Pretraining — Sean Welleck (Fall 2025) · YouTube — Covers: masked language modeling objective; autoregressive language modeling objective.

  • CS 2881: AI Safety (Harvard, Boaz Barak)
    Course site · YouTube playlist
    Research-focused course on the theoretical foundations of modern ML and AI safety. Topics include generalization and sample complexity, optimization and implicit bias, robustness and distribution shift, interpretability, alignment and safety, and open problems.

  • CS329H: Machine Learning from Human Preferences (Stanford)
    Course site · YouTube playlist
    Advanced topics at the intersection of ML and human feedback: preference modeling, RLHF/GRPO, reward modeling and dataset construction, safety and alignment considerations, evaluation beyond accuracy, and applications to LLMs and agents.

  • CS224W: Machine Learning with Graphs (Stanford)
    Course site · Videos
    Representation learning on graphs/networks: node and graph embeddings, GNNs, message passing, link prediction, graph reasoning, and applications.

  • CS-E4740: Federated Learning (Aalto University, Spring 2025)
    Course site · YouTube playlist
    Instructor: Alexander Jung. Syllabus highlights: FL networks, design principles, core algorithms, flavors/variants, and trustworthy FL; weekly lectures and exercises with Python/Jupyter assignments. This playlist contains material for CS-E4740 Federated Learning offered at Aalto University during Spring 2025.

  • HAII-2024: Human-AI Interaction (Politecnico di Torino)
    YouTube playlist
    Lectures from the Ph.D. course "Human-AI Interaction" (2024). Focus on methods and systems for designing, building, and evaluating human–AI interactions; covers prototyping, UX for AI, evaluation methods, and case studies. Instructors: Luigi De Russis, Alberto Monge Roffarello.

  • HAI Fall Conference 2022: AI in the Loop (Stanford HAI)
    YouTube playlist
    Human-in-the-loop artificial intelligence refers to AI decision-making processes where humans may provide feedback or confirmation. This conference challenges the phrase and explores a future where humans remain at the center of all AI technologies.

Coming soon — self‑paced and cohort options.