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- Homepage
- https://sites.google.com/view/cos-511-spring-2026/home
- About
- One of the most-cited researchers in online convex optimization and regret minimization; Princeton CS professor, co-founder of Google AI Princeton, and author of the standard text Introduction to Online Convex Optimization.
- Topics
- Statistical learning theory · Online learning and regret · Convex optimization · Learning with partial observability · Control theory · Reinforcement learning in dynamical systems
- Notes
- Hazan's proof-based graduate course on the theory of machine learning, reframed in the 2026 edition around learning and control in dynamical systems. New this year: the full lecture series is recorded and posted publicly on Hazan's own YouTube channel, with scribe notes and exercises on the course site. A rare chance to watch a leading online-optimization theorist teach the subject end to end.
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- Homepage
- https://scenerepresentations.org/courses/2026/spring/advances-in-cv/
- About
- Durand is a leading computational-photography and rendering researcher; Sitzmann is known for neural implicit scene representations (NeRF-adjacent work).
- Topics
- Multi-view and projective geometry · Neural scene representations · Geometric deep learning · Diffusion models · Differentiable rendering · Embodied vision for robotics
- Notes
- Graduate course at the frontier of computer vision, organized in modules on geometry, representation learning, and embodied AI — from projective geometry and 3D scene understanding through diffusion and differentiable rendering to robotic perception. The Spring 2026 lecture recordings are openly viewable on MIT's hosted Panopto (no login required) and per-lecture slides are shared on Google Drive.
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- Homepage
- https://bvasiles.github.io/empirical-methods/
- About
- CMU professor in the Software and Societal Systems Department; known for empirical software-engineering research mining large-scale data from GitHub, Stack Overflow, and open-source communities.
- Topics
- Research design · Interviews and qualitative coding · Survey design · Statistical modeling · Mining software repositories · Social network analysis
- Notes
- A PhD-level course on rigorous empirical research methodology, spanning qualitative methods (interviews, coding, surveys) and quantitative analysis (regression, mixed models, network analysis), with a thread on extracting and integrating data from software repositories. Lecture videos are openly posted on a public YouTube channel and slides are linked per session; only the assigned readings sit behind a CMU login.
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- Homepage
- https://cme296.stanford.edu/
- About
- The Amidi brothers are known for creating the widely-used machine learning cheatsheets for Stanford's CS229 and CS230 courses, now used by millions of learners worldwide.
- Topics
- Diffusion models · Score matching and flow matching · Diffusion Transformers and U-Nets · Controllable image generation · Model evaluation · Video generation
- Notes
- An 8-lecture Stanford graduate course on diffusion-based generative models for vision, covering the full stack from DDPM and score matching through modern architectures like U-Nets and Diffusion Transformers. Includes controllable generation, model training and finetuning, and evaluation metrics. Lecture slides are released publicly alongside YouTube recordings as the Spring 2026 course progresses.
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- Homepage
- https://introtodeeplearning.com/
- About
- Alexander Amini leads MIT's deep autonomy research group; both instructors have run this annually-updated course since 2017, making it one of the most widely-viewed open MIT deep learning courses.
- Topics
- Deep learning fundamentals · Sequence modeling · Generative modeling · Reinforcement learning · Large language models · AI for science
- Notes
- MIT's annual short-course introduction to deep learning, updated for 2026 with new modules on AI for science, massively parallel training, and AI ethics. All lecture slides are open-sourced under MIT license and three practical labs are available on GitHub covering music generation, facial detection, and LLM fine-tuning.
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- Homepage
- https://safari.ethz.ch/architecture/fall2025/
- About
- ETH professor of computer science (previously CMU); his group's work on DRAM-aware system design and the discovery of RowHammer shaped modern memory-system research, and his lecture series have been a staple open resource for computer architecture for over a decade.
- Topics
- Instruction set architecture · Pipelining and branch prediction · Caches and memory hierarchy · Virtual memory · Prefetching · Multiprocessors and accelerators
- Notes
- Mutlu's graduate computer-architecture course in its Fall 2025 edition, with lectures posted to a public YouTube channel as the term proceeds and per-lecture slides and reading material openly hosted on the SAFARI group site. The companion lab designs a MIPS-style pipelined processor in SystemVerilog. A flagship open course on how modern hardware actually works under the software.
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- Homepage
- https://cme295.stanford.edu/
- About
- Stanford adjunct lecturers known for the widely-used ML cheatsheets that accompany CS229 and CS230; also teach the catalog's CME296 diffusion course.
- Topics
- Transformer architecture · LLM training and fine-tuning · Preference tuning and RLHF · Reasoning models · Retrieval-augmented generation and agents · LLM evaluation
- Notes
- A compact 9-lecture Stanford graduate course on Transformers and LLMs, organized as a guided tour from the attention mechanism through pretraining, fine-tuning, reasoning, RAG, agents, and evaluation. The full Autumn 2025 playlist is on YouTube alongside per-lecture slides; midterm and final exams with solutions are posted on the course site.
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- Homepage
- https://cmu-l3.github.io/anlp-fall2025/
- About
- CMU Language Technologies Institute faculty; works on language-model reasoning, mathematical reasoning, and code generation, and co-leads the L3 (Language, Learning, and Logic) Lab.
- Topics
- Transformers and attention · Pretraining and fine-tuning · Decoding and inference strategies · Retrieval-augmented generation · Reinforcement learning and agents · Mixture of experts and long-sequence models
- Notes
- CMU's graduate Advanced NLP course in its Fall 2025 edition — Welleck rebuilds the canon around modern LLMs, with sessions on architectures, pretraining and post-training, decoding, multimodal and long-sequence models, and RL-based reasoning. ~25 lectures are posted to a public YouTube playlist as the course proceeds, with slides and code repositories linked from the schedule. A useful complement to the Stanford NLP courses already in the catalog.
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- Homepage
- https://president.yale.edu/committees-programs/devane-lectures/america-at-250-a-history
- Topics
- U.S. political history 1776–present · Race and Reconstruction · Cold War and national security · American identity
- Notes
- One-time-only Yale course for the nation’s 250th anniversary, asking what America is and was meant to be. Three eminent historians, each with a distinct lens. Weekly post-lecture discussions by the professors are also posted to YouTube.
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- Homepage
- https://stanford-cs221.github.io/autumn2025/
- About
- Directs Stanford’s Center for Research on Foundation Models (CRFM); known for benchmarking LLMs.
- Topics
- Machine learning · Search · Markov decision processes · Bayesian networks · Logic · Language models
- Notes
- Stanford’s flagship AI course, rigorous and broad.
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- Homepage
- https://www.youtube.com/playlist?list=PLGhmZX2NKiNm2iEUtVslIUHTW9i2zAG72
- About
- Columbia microbiology and immunology professor whose lab established the first infectious DNA clone of an animal RNA virus (poliovirus); runs the long-running This Week in Virology podcast and the MicrobeTV channel, and is one of the most visible public teachers of modern virology.
- Topics
- Viral structure and genomes · Replication strategies · Pathogenesis and host response · Immunity to viruses · Vaccines and antivirals · Emerging viruses
- Notes
- Racaniello's full 25-lecture Spring 2025 virology course at Columbia, posted to YouTube as the term progressed. Covers what viruses are and what they do — structure, genomes, replication strategies, immunity, vaccines, antivirals, and emerging diseases — at an upper-undergraduate level, by one of the field's most committed open-science teachers. A rare full-semester biology course in the catalog.
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- Homepage
- https://cs336.stanford.edu/
- About
- Liang directs Stanford's Center for Research on Foundation Models (CRFM); Hashimoto works on language model evaluation and robustness.
- Topics
- Tokenization · Transformer architectures · GPU kernels · Parallelism · Scaling laws · LLM evaluation
- Notes
- A systems-level course that builds a language model from scratch — tokenizer, attention, GPU kernels, data pipelines, and scaling laws. Unusually implementation-heavy even for a Stanford graduate course, with all five assignments on GitHub. Full 2025 lecture series released publicly on YouTube.
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- Homepage
- https://optimalcontrol.ri.cmu.edu/
- About
- Works on trajectory optimization, spacecraft dynamics, and fast numerical methods for robot motion planning.
- Topics
- LQR · Trajectory optimization · iLQR and DDP · State estimation · System identification · Reinforcement learning
- Notes
- A graduate robotics course on controlling real physical systems: covers classical optimal control (Pontryagin, Riccati, LQR), numerical trajectory optimization, and how these connect to modern RL. Strong on both theory and implementation; lecture notes and Jupyter notebooks are posted with each lecture.
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- Homepage
- https://ocw.mit.edu/courses/18-156-projection-theory-spring-2025/
- About
- Known for major work in geometric combinatorics and harmonic analysis.
- Topics
- Projection theorems · Geometric measure theory · Additive combinatorics · Harmonic analysis · Homogeneous dynamics
- Notes
- A recent graduate analysis course built around a field that had major breakthroughs very recently. The OCW release is unusually complete: full lecture videos, polished notes, and weekly problem sets.
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- Homepage
- https://ocw.mit.edu/courses/18-100b-real-analysis-spring-2025/
- About
- Geometric analyst known for work on minimal surfaces and geometric PDE.
- Topics
- Real numbers · Proof techniques · Continuity · Differentiation · Riemann integration
- Notes
- Fresh 2025 OCW capture of MIT's core analysis sequence, with the standard epsilon-delta backbone presented in full lecture-video form. A strong seed entry for anyone wanting a rigorous modern baseline in pure math.
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- Homepage
- https://cs50.harvard.edu/extension/sql/2025/spring/
- Topics
- Relational databases · SQL querying · Schema design · Views and CTEs · Indexes · Scaling
- Notes
- A clean, focused databases course with on-demand lecture videos and a full progression from basic querying through indexing and replication. More practical than theoretical, but unusually well-scaffolded and easy to enter.
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- Homepage
- https://mit-mi.github.io/how2ai-course/spring2025/
- Topics
- Multimodal AI · Foundation models · Medical and sensory data · Audio and video
- Notes
- Graduate seminar on applying modern AI to unconventional data types. Less about any one application, more about the research mindset for tackling new modalities.
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- Homepage
- https://cs50.harvard.edu/hls/2025/winter/
- About
- Built CS50 into one of the most visible publicly available computer-science course families.
- Topics
- Programming · Algorithms · SQL · Artificial intelligence · Web basics · Privacy and security
- Notes
- An accelerated January course for law students that explains technical systems in enough depth to reason about their legal consequences. More interdisciplinary than the usual CS50 spinoff, with daily assignments and a tight lecture sequence.
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- Homepage
- https://eyolfson.com/courses/archive/utoronto/ece454/2024-fall/
- About
- U of T ECE Assistant Professor in the Teaching Stream; previously taught operating systems at UCLA and compilers at Waterloo, where he won the Chakma Award for Exceptional Teaching.
- Topics
- Performance profiling · Compiler optimization · Memory hierarchy and caches · Dynamic memory allocation · Threading and synchronization · Rust for systems
- Notes
- A 20-lecture upper-level course on writing systems software for performance: profiling, compiler optimizations, cache-conscious code, custom allocators, parallel and concurrent programming, and an unusual segment introducing Rust as a systems language. Slides, per-lecture YouTube videos, exams, and the public lab documentation site are all on the instructor's archive.
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- Homepage
- https://eyolfson.com/courses/archive/utoronto/ece344/2024-fall/
- About
- U of T ECE Assistant Professor in the Teaching Stream; previously taught operating systems at UCLA and compilers at Waterloo, where he won the Chakma Award for Exceptional Teaching.
- Topics
- Processes and threads · Scheduling · Synchronization · Virtual memory · Filesystems · Virtualization
- Notes
- A 35-lecture undergraduate operating systems course built around six C labs targeting the OS161 instructional kernel. Lecture videos, slides, midterm and final exams, and lab documentation (process viewer through filesystems) are all openly hosted on the instructor's archive and the compeng-gg lab site.
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- Homepage
- https://ocw.mit.edu/courses/6-s890-topics-in-multiagent-learning-fall-2024/
- About
- MIT EECS faculty whose research sits at the intersection of game theory, optimization, and learning; contributed to the Pluribus and Cicero superhuman game-playing systems.
- Topics
- Matrix games and equilibrium concepts · Imperfect-information games · Structured games (combinatorial, polymatrix, stochastic) · Equilibrium computation and learning dynamics · Computational complexity of equilibrium · Applications to superhuman game-playing AI
- Notes
- Graduate seminar synthesizing game theory, optimization, and learning for settings where multiple agents with conflicting objectives learn simultaneously. Builds from matrix games to richer structured games and connects the theory to recent superhuman AI in poker, Diplomacy, and Stratego.
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- Homepage
- https://ocw.mit.edu/courses/21h-151-dynastic-china-fall-2024/
- About
- MIT historian and the S. C. Fang Career Development Associate Professor of Chinese Language and Culture.
- Topics
- Imperial Chinese state formation · Chinese political thought · Dynastic transitions · Gender and social life · Commercial history · China in global context
- Notes
- A survey of Chinese history from the earliest dynasties to 1800, organized around state formation, intellectual life, commerce, and everyday society. The OCW release includes publicly watchable lecture videos, the full syllabus, and reading lists, making it a strong recent history addition to the catalog.
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- Homepage
- https://ocw.mit.edu/courses/6-7960-deep-learning-fall-2024/
- About
- Isola is known for influential work in image-to-image translation and representation learning; Beery for applying ML to ecological monitoring; Bernstein for optimization theory in deep networks.
- Topics
- Neural network architectures · Learning theory · Backpropagation · Transformers · Geometry and invariances
- Notes
- A rigorous MIT graduate course on deep learning foundations, covering architecture families (CNNs, RNNs, graph nets, transformers) alongside approximation theory, generalization in high dimensions, and the geometry of learned representations. Unusually theory-forward for a deep learning course; fresh 2024 recording with full lecture notes and problem sets.
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- Homepage
- https://ocw.mit.edu/courses/6-4590-foundations-of-information-policy-fall-2024/
- Topics
- Internet governance · Privacy · Cybersecurity · Freedom of expression · Intellectual property · AI policy
- Notes
- A policy-facing MIT course on how technical architecture and law shape the internet. The 2024 edition explicitly ties classic internet-policy debates to current AI questions, and the OCW release includes lecture notes, readings, and written assignments.
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- Homepage
- https://ocw.mit.edu/courses/14-41-public-finance-and-public-policy-fall-2024/
- About
- Prominent public-finance and health-economics scholar; widely associated with the design of the Affordable Care Act.
- Topics
- Externalities · Public goods · Education policy · Health economics · Taxation · Social insurance
- Notes
- A full public-finance sequence with videos, handouts, problem sets, and solutions. Broad policy coverage rather than narrow technical specialization, which makes it a strong conceptual seed for economics on the page.
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- Homepage
- https://cs50.harvard.edu/extension/ai/2024/fall/
- Topics
- Search · Knowledge representation · Probabilistic inference · Constraint satisfaction · Neural networks · Language
- Notes
- A compact survey of core AI ideas with one substantial project per unit. The material is broad rather than research-frontier, but it is a reliable open sequence that covers the classical conceptual map clearly.
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- Homepage
- https://web.stanford.edu/class/cs234/CS234Spr2024/index.html
- About
- Known for work on safe RL, bandit algorithms, and efficient exploration; directs Stanford's AI safety and education research.
- Topics
- Markov decision processes · Policy gradients · Q-learning · Offline RL · Exploration · Value alignment
- Notes
- Stanford's main RL course, updated in 2024 with new content on DPO, offline RL, and LLM alignment. Includes a guest lecture on Direct Preference Optimization by the method's first authors. Full playlist publicly available on YouTube.
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- Homepage
- https://deeplearning.cs.cmu.edu/S24/index.html
- About
- Raj and Singh are both Carnegie Mellon faculty known for speech, audio, and privacy-preserving machine learning.
- Topics
- MLPs · CNNs · RNNs · Attention mechanisms · Graph neural networks · Generative models
- Notes
- CMU's comprehensive deep learning sequence covering the full architecture family with thorough mathematical grounding. Unusually broad, with bootcamp labs on software foundations alongside lectures. 28 lectures publicly available on YouTube.
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- Homepage
- https://ocw.mit.edu/courses/9-35-perception-spring-2024/
- About
- Leads MIT's Laboratory for Computational Audition; known for computational and psychophysical research on how humans perceive complex soundscapes.
- Topics
- Auditory perception · Visual system · Psychophysics · Color and motion perception · Object recognition · Chemical senses
- Notes
- A complete MIT undergraduate course covering the science of perception across the major senses, with emphasis on audition and vision. Uses illusion labs to probe perceptual mechanisms and applies psychophysical methods to quantify sensory thresholds and phenomena. The 23-lecture sequence was recorded in 2023–2024 and is freely available with full problem sets.
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- Homepage
- https://web.stanford.edu/class/cs224n/
- About
- Co-author of the standard NLP textbooks and leads the Stanford NLP Group; known for foundational work in parsing, named entity recognition, and information extraction.
- Topics
- Word vectors · Transformers · Pre-training · Post-training · LLM agents · Benchmarking and reasoning
- Notes
- The canonical graduate NLP course at Stanford, updated for 2024 with new content on post-training, RLHF, reasoning, and agents. Lecture notes cover roughly the first half of the course. Full YouTube playlist is publicly available.
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- Homepage
- https://deepgenerativemodels.github.io/
- About
- Stanford CS professor whose group co-invented score-based generative modeling and contributed core ideas to diffusion models; works at the interface of generative modeling and probabilistic inference.
- Topics
- Autoregressive models · Variational autoencoders · Normalizing flows · Generative adversarial networks · Energy-based models · Score-based and diffusion models
- Notes
- An 18-lecture graduate tour of generative modeling: autoregressive models, VAEs, flows, GANs, energy-based models, and the score-matching/diffusion stack the instructor's own group helped invent. The theoretical thread (score matching → Langevin → SDE/ODE duality) is exactly where modern math-of-ML lives. Full 2023 playlist on YouTube with slides on the course site.
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- Homepage
- https://rail.eecs.berkeley.edu/deeprlcourse/
- About
- Pioneer in deep RL, offline RL, and robot learning; leads Berkeley's Robotic AI and Learning Lab.
- Topics
- Imitation learning · Policy gradients · Actor-critic methods · Model-based RL · Inverse RL · Meta-learning
- Notes
- Berkeley's flagship deep RL course, known for rigorous mathematical treatment and research-frontier coverage. 23 lectures spanning classical policy optimization through offline RL, exploration, and meta-learning. The most recent publicly available full-semester recording.
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- Homepage
- https://web.stanford.edu/class/ee364a/
- About
- Stanford EE professor and author (with Lieven Vandenberghe) of the standard text Convex Optimization; a central figure in bringing convex optimization to engineering and ML practice.
- Topics
- Convex sets and functions · Duality and KKT conditions · Linear and quadratic programming · Semidefinite and conic optimization · Gradient and Newton methods · Applications across ML, control, and statistics
- Notes
- The canonical course on convex optimization, recorded fresh in 2023 with the full 18-lecture sequence on YouTube. Boyd's lectures move quickly between geometric intuition, duality, and concrete algorithms, and pair tightly with the free textbook. Foundational background for ML theory, control, and statistics.
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- Homepage
- https://dlsyscourse.org/
- About
- Chen created TVM, XGBoost, and Apache MXNet; Kolter is known for implicit layers, equilibrium networks, and robust optimization.
- Topics
- Automatic differentiation · GPU computation · Neural network compilers · Operator fusion · Backpropagation implementation
- Notes
- A course about how deep learning frameworks actually work under the hood — students implement the Needle library from scratch, covering autodiff, CUDA ops, and compiler optimizations. A rare course that bridges ML and systems at a research depth. Individual 2022 lecture videos are publicly on YouTube.
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- Homepage
- https://www.math.uci.edu/~rvershyn/teaching/hdp/hdp.html
- About
- UC Irvine mathematician and author of the standard reference High-Dimensional Probability: An Introduction with Applications in Data Science; works on random matrices, concentration, and probability in data science.
- Topics
- Sub-gaussian and sub-exponential distributions · Concentration inequalities · Random matrices and covariance estimation · Johnson–Lindenstrauss dimension reduction · Empirical processes and uniform laws · Sparse recovery and compressed sensing
- Notes
- 41 video lectures that build the probabilistic toolbox underpinning modern theoretical research in ML, theoretical CS, statistics, and signal processing — concentration, random matrices, dimension reduction, and beyond. Vershynin teaches directly from his textbook with 13 problem sets posted alongside the videos. Exactly the math-of-ML-theory backbone the catalog was missing.
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- Homepage
- https://web.stanford.edu/class/stats214/
- About
- Stanford CS professor focused on the theory of deep learning, optimization landscapes, and the science of foundation models; one of the most active young researchers in modern ML theory.
- Topics
- Uniform convergence and generalization bounds · Implicit and algorithmic regularization · Non-convex optimization landscapes · Neural tangent kernel · Theory of representation learning · Bandits and online learning
- Notes
- A graduate course that asks why ML algorithms work — generalization, optimization landscapes, NTK, implicit regularization, representation learning — taught by one of the most prolific researchers writing this theory. Full Fall 2021 playlist on YouTube with detailed lecture notes online. A direct bullseye for an ML-theory career direction.
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- Homepage
- https://atcold.github.io/NYU-DLSP21/
- About
- LeCun is a Turing Award laureate (2018) and Meta's Chief AI Scientist; co-developed convolutional networks and energy-based learning. Canziani is an NYU faculty fellow known for clear pedagogy on representation learning.
- Topics
- Supervised and self-supervised learning · Energy-based models · Convolutional and recurrent architectures · Embedding methods and metric learning · Generative models · Vision, language, and speech applications
- Notes
- LeCun and Canziani's deep-learning course as taught at NYU CDS — the closest you can get to LeCun's own framing of the field, with a heavy emphasis on energy-based and self-supervised learning rather than the usual feed-forward tour. Closed-captioned lectures, written overviews, and executable PyTorch notebooks are all openly online.
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- Homepage
- https://cs182sp21.github.io/
- Topics
- Backpropagation · CNNs · RNNs · Transformers · Meta-learning · Generative models
- Notes
- Berkeley's undergraduate deep learning course taught by Levine. Covers the full arc from backpropagation through meta-learning, with 21 lectures and 4 homework assignments. A solid public complement to CS285 for learners at the undergrad level.
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- Homepage
- https://ocw.mit.edu/courses/14-13-psychology-and-economics-spring-2020/
- About
- MIT development economist whose fieldwork on poverty and mental health in low-income settings has shaped behavioral public policy research.
- Topics
- Time preferences and self-control · Risk preferences · Social preferences and reciprocity · Limited attention · Default effects and nudges · Poverty and psychology
- Notes
- A behavioral economics course that systematically applies psychological research to economic questions across labor, finance, health, and development. Unlike standard microeconomics, 14.13 builds models around how humans actually decide — covering time inconsistency, reference dependence, social norms, and attention. The OCW release includes 23 video lectures, lecture notes, slides, and full problem sets with solutions.
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- Homepage
- https://web.eecs.umich.edu/~justincj/teaching/eecs498/FA2019/
- About
- UMich assistant professor and co-developer of Stanford's CS231n; known for work on visual relationship detection, image captioning, and neural rendering.
- Topics
- Linear classifiers and backpropagation · Convolutional and recurrent networks · Attention and transformers · Object detection and segmentation · Generative models (GANs, VAEs) · Deep reinforcement learning
- Notes
- The canonical open successor to Johnson's CS231n — 22 self-contained lectures that build deep learning for vision from linear classifiers through transformers, detection, generative models, and RL, taught by one of CS231n's original instructors. The full Fall 2019 playlist is on YouTube with slides and six PyTorch/Colab assignments on the course site.
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- Homepage
- https://ocw.mit.edu/courses/9-13-the-human-brain-spring-2019/
- About
- MIT cognitive neuroscientist who discovered the fusiform face area and pioneered the functional imaging of category-selective regions of the human cortex.
- Topics
- Functional brain imaging methods · Face and place perception · The visual word form area · Number and language regions · The theory-of-mind network · Cortical organization of cognition
- Notes
- An undergraduate tour of what fMRI and related methods reveal about the functional architecture of the human mind, organized around the specialized cortical regions Kanwisher's own lab helped discover. The full Spring 2019 lecture series is on OCW and YouTube with assignments and readings.
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- Homepage
- http://www.cs.cmu.edu/~odonnell/complexity17/
- About
- Known for Boolean function analysis and the textbook 'Analysis of Boolean Functions'; longstanding CMU theoretician.
- Topics
- Time and space hierarchy theorems · Circuit complexity · Randomized complexity · Interactive proofs · PCP theorem · Hardness amplification
- Notes
- A rigorous graduate course in computational complexity, covering the classical hierarchy from P and NP through circuit lower bounds, IP=PSPACE, and the PCP theorem. O'Donnell posts comprehensive handwritten lecture notes alongside 27 video lectures. One of the few fully public graduate complexity courses online.
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- Homepage
- https://ocw.mit.edu/courses/18-650-statistics-for-applications-fall-2016/
- About
- MIT applied math professor working on high-dimensional statistics, optimal transport, and statistical learning theory; known for clean, rigorous lectures.
- Topics
- Parametric inference and MLE · Method of moments and asymptotics · Hypothesis testing · Goodness of fit · Linear and generalized linear regression · Bayesian inference and principal component analysis
- Notes
- A rigorous undergraduate statistics course taught at the standard a theory-curious student needs — parametric inference, asymptotics, testing, regression, GLMs, and PCA, all derived rather than asserted. Lectures, slides, and problem sets are openly hosted on OCW and YouTube. A natural prerequisite ramp into ML theory.
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- Homepage
- https://davidstarsilver.wordpress.com/teaching/
- About
- DeepMind research lead and the principal architect of AlphaGo, AlphaZero, and MuZero; one of the central figures in the deep-RL revolution.
- Topics
- Markov decision processes · Dynamic programming · Monte Carlo and TD methods · Value function approximation · Policy gradients · Exploration and integration with planning
- Notes
- Silver's 10-lecture UCL course that introduced a generation to reinforcement learning — clean, slide-driven, and tightly mapped to Sutton & Barto. Still the most pedagogically polished public introduction to the field. Full playlist on the official DeepMind channel with slides linked from his teaching page.
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- Homepage
- https://www.stevenstrogatz.com/teaching
- About
- Author of the canonical textbook on nonlinear dynamics and widely known for public writing on mathematics; based at Cornell Applied Mathematics.
- Topics
- Phase plane analysis · Bifurcations · Limit cycles · Lorenz equations · Chaos and strange attractors · Fractals
- Notes
- Strogatz's full 25-lecture graduate course filmed at Cornell, closely following his textbook. Covers 1D flows through chaos in the Lorenz system, with biological and physical applications throughout. Geometric and intuitive style that remains the gold standard introduction to the field.
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- Homepage
- https://timroughgarden.org/f13/f13.html
- About
- Known for the price of anarchy in network routing and auction theory; now at Columbia after many years at Stanford.
- Topics
- Mechanism design · Vickrey and Myerson auctions · Price of anarchy · Selfish routing · No-regret learning · Nash equilibrium complexity
- Notes
- A graduate course at the intersection of algorithms and economics: auctions, mechanism design, equilibrium analysis, and the price of anarchy in networks. 20 video lectures plus comprehensive polished lecture notes. The standard reference for algorithmic game theory as a graduate topic.
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- Homepage
- https://work.caltech.edu/telecourse.html
- About
- Caltech professor of electrical engineering and computer science; known for machine learning and financial forecasting and for the concise textbook Learning From Data.
- Topics
- Learning feasibility and Hoeffding's inequality · VC dimension and generalization · Bias–variance tradeoff · Linear models and gradient descent · Regularization and validation · Support vector machines and kernels
- Notes
- A balanced, theory-first introduction to machine learning that earns its reputation for clarity: 18 hour-long lectures recorded live at Caltech in 2012, anchored by the VC analysis of generalization rather than a tour of algorithms. The course site hosts the videos, slides, eight homework sets, and solution keys.
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- Homepage
- https://ocw.mit.edu/courses/6-262-discrete-stochastic-processes-spring-2011/
- About
- MIT information theorist; co-inventor of LDPC codes and author of foundational texts on information theory, digital communication, and stochastic processes.
- Topics
- Poisson and renewal processes · Markov chains: finite and countable state · Random walks and martingales · Markov processes in continuous time · Queueing and large deviations
- Notes
- A 25-lecture graduate stochastic-processes course taught by one of MIT's great teachers, threading from Poisson and renewal processes through countable-state Markov chains, random walks, martingales, and large deviations. Full video on OCW with course notes, weekly problem sets, and solutions — a complete self-study package.
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- Homepage
- https://ocw.mit.edu/courses/6-034-artificial-intelligence-fall-2010/
- About
- Longtime director of the MIT AI Laboratory and author of a foundational AI textbook; his lectures on representation and learning shaped how generations of students think about the field.
- Topics
- Goal trees and rule-based systems · Search and constraint propagation · Logic and reasoning · Learning: nearest neighbors and identification trees · Neural nets and SVMs · Representation and architectures
- Notes
- A conceptual, big-picture tour of classical AI from one of its great teachers — reasoning, search, constraints, and the learning methods that predate the deep-learning era, all motivated by how intelligence is represented. The full Fall 2010 lecture series is on OCW and YouTube with notes, assignments, and exams with solutions.
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- Homepage
- https://www.youtube.com/playlist?list=PL848F2368C90DDC3D
- About
- Stanford professor of biology and neurology and MacArthur Fellow; known for decades of fieldwork on stress in wild baboons and for the books Why Zebras Don't Get Ulcers and Behave.
- Topics
- Behavioral evolution · Molecular genetics and heritability · Ethology and neuroscience · Aggression and cooperation · Sexual behavior · Individual differences and psychiatric disorders
- Notes
- Sapolsky's legendary 25-lecture Stanford course on why humans and animals behave as they do, refusing single-cause explanations and instead layering evolution, genes, hormones, neurobiology, and environment. One of the most-watched university lecture series ever recorded, filmed in full and posted to YouTube. A near-perfect fit for evolutionary biology and behavior.
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- Homepage
- https://ocw.mit.edu/courses/18-06-linear-algebra-spring-2010/
- About
- MIT mathematician renowned for his linear algebra textbooks and for a teaching style that foregrounds geometric and computational intuition over rote manipulation.
- Topics
- Systems of equations and elimination · Vector spaces and subspaces · Orthogonality and least squares · Determinants · Eigenvalues and diagonalization · Positive definite and singular value decomposition
- Notes
- The linear algebra course that taught a generation, built around the four fundamental subspaces and Strang's insistence on understanding matrices as actions rather than tables of numbers. Among the most-watched mathematics lecture series anywhere; full video, problem sets, and exams with solutions are free on OCW.
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- Homepage
- https://justiceharvard.org/
- About
- Harvard political philosopher and communitarian critic of Rawlsian liberalism; author of Justice and What Money Can't Buy.
- Topics
- Utilitarianism · Libertarianism · Kantian ethics · Rawls and distributive justice · Affirmative action and markets · Citizenship and the common good
- Notes
- The first Harvard course made freely available online and on public television — Sandel leads a thousand-student lecture hall through moral dilemmas, building from utilitarianism to Kant, Rawls, and Aristotle. Twelve two-part episodes are posted in full with the trolley problem and other cases. A flagship of political theory and applied ethics.
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- Homepage
- https://oyc.yale.edu/ecology-and-evolutionary-biology/eeb-122
- About
- Edward P. Bass Professor at Yale; pioneer in life history theory and a founder of evolutionary medicine; authored The Evolution of Life Histories and co-authored Evolution: An Introduction; founded the European Society for Evolutionary Biology.
- Topics
- Natural selection and genetic drift · Life history evolution · Sexual selection and mating systems · Speciation and phylogenetics · Evolutionary medicine · Ecology and biodiversity
- Notes
- A sweeping 36-lecture undergraduate course covering modern evolutionary biology from population genetics through speciation, ecology, and animal behavior. Stearns extends the arc into evolutionary medicine and the implications of evolutionary thinking for the social sciences — unusually broad scope for a single course. Produced by Open Yale Courses under a Creative Commons license and freely available.
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- Homepage
- https://oyc.yale.edu/economics/econ-252-08
- About
- Nobel laureate (2013) in economics; pioneer of behavioral finance and asset-price volatility research, creator of the Case–Shiller home-price index, and author of Irrational Exuberance.
- Topics
- Risk and portfolio diversification · Behavioral finance · Debt and equity markets · Insurance and banking · Real estate finance · Derivatives and regulation
- Notes
- Shiller's 26-lecture survey of finance as a social institution — its theory, its history, and its imperfections — recorded just as the 2008 crisis was unfolding, which gives the behavioral-finance material unusual immediacy. Full video, transcripts, and problem sets are free on Open Yale Courses.
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- Homepage
- https://oyc.yale.edu/economics/econ-159
- About
- Professor of Economics and Management at Yale; known for his work on mechanism design and the history of economic thought.
- Topics
- Dominance · Nash equilibrium · Backward induction · Evolutionary stability · Asymmetric information · Auctions
- Notes
- One of the most celebrated open lecture series on game theory — 24 lectures that build from basic strategic reasoning to mechanism design, adverse selection, and signaling. Polak's teaching style is exceptionally clear and Socratic. Transcripts, problem sets, and video all free on Open Yale Courses.
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- Homepage
- https://oyc.yale.edu/psychology/psyc-110
- About
- Cognitive and developmental psychologist (then at Yale, now University of Toronto); known for work on the origins of morality and pleasure and the book Against Empathy.
- Topics
- Neuroscience and the brain · Perception and learning · Memory and cognition · Development and language · Social psychology · Mental illness and happiness
- Notes
- A wide-ranging survey of the scientific study of mind and behavior, from neurons and Freud to language, morality, and mental illness, delivered in Bloom's lucid and frequently funny lecture style. Full video, transcripts, and syllabus are free on Open Yale Courses under a Creative Commons license.
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- Homepage
- https://oyc.yale.edu/philosophy/phil-176
- About
- Clark Professor of Philosophy at Yale; works in normative ethics and is the author of Death and Normative Ethics.
- Topics
- Dualism vs. physicalism · Personal identity · The nature of death · Whether death is bad · Immortality and the value of life · Suicide and rationality
- Notes
- A rigorous yet accessible 26-lecture philosophy course confronting a single question — what does it mean to die, and is death bad for the one who dies? Kagan argues from a physicalist standpoint through personal identity, the badness of death, and how mortality should shape how we live. Full video and transcripts free on Open Yale Courses.
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- Homepage
- https://groups.csail.mit.edu/mac/classes/6.001/abelson-sussman-lectures/
- About
- MIT computer scientists and co-authors of Structure and Interpretation of Computer Programs, the canonical Scheme-based introduction to computing used at MIT for two decades; both have shaped how generations of programmers think about abstraction, modularity, and language design.
- Topics
- Lisp and Scheme · Higher-order procedures · Data abstraction and compound data · Assignment, state, and streams · Metacircular evaluator and logic programming · Register machines, compilation, and garbage collection
- Notes
- The legendary 1986 SICP video lectures, recorded for Hewlett-Packard employees: 20 hours of Abelson and Sussman teaching MIT's introductory CS course around procedures, data abstraction, streams, a metacircular Scheme evaluator, and a small compiler. Decades on, still one of the most-watched and most-loved computer-science lecture series, paired with the freely available SICP textbook as the de facto course notes.
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- Homepage
- https://www.cs.cornell.edu/courses/cs6120/2025fa/self-guided/
- About
- Cornell CS professor working on programming languages, computer architecture, and approximate computing; designer of the Bril intermediate language used as the substrate for the course's implementation tasks.
- Topics
- Intermediate representations and SSA · Data-flow analysis · LLVM · Loop and interprocedural optimization · Garbage collection and JIT compilation · Concurrency and parallelism
- Notes
- Sampson's PhD-level Cornell compilers course, packaged as a self-guided online sequence of 14 lessons that anyone can follow at their own pace. Each lesson has lecture videos, written notes, assigned papers, and open-ended implementation tasks built on the course's Bril intermediate language, with a public GitHub discussion forum for non-Cornell students.
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- Homepage
- https://www.complexityexplorer.org/courses/165-introduction-to-complexity
- About
- SFI external professor and Portland State computer scientist; known for analogy-making (the Copycat project), genetic algorithms, and the books Complexity: A Guided Tour and Artificial Intelligence: A Guide for Thinking Humans.
- Topics
- Dynamics and chaos · Fractals · Information theory and entropy · Cellular automata · Genetic algorithms · Networks and self-organization
- Notes
- The Santa Fe Institute's flagship introduction to complexity science, taught in video units by Melanie Mitchell across dynamics, information, cellular automata, genetic algorithms, and networks — showing how emergence and self-organization recur across nature and society. Free with registration on Complexity Explorer, with unit quizzes and exercises. A direct hit on complexity and emergence.
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- About
- Yale professor of German and Comparative Literature; co-editor of the new Princeton critical edition of Capital, Volume 1 (2024).
- Topics
- Political economy · Capital and labor · Value and commodities · Class struggle · Historical materialism · Accumulation
- Notes
- A chapter-by-chapter close reading of Capital Volume 1 taught by the co-editor of the 2024 Princeton critical edition. Nineteen lectures trace Marx's argument from commodity fetishism through the working day to primitive accumulation, with close attention to the philosophical architecture of the text.