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Princeton COS 511 Theoretical Machine Learning (Spring 2026)Elad Hazan
- Video
- YouTube playlist ↗
- Homepage
- https://sites.google.com/view/cos-511-spring-2026/home
- Topics
- Statistical learning theory · Online learning and regret · Convex optimization · Learning with partial observability · Control theory · Reinforcement learning in dynamical systems
- Prerequisites
- Probability, linear algebra, real analysis; mathematical maturity.
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MIT 6.8300 Advances in Computer Vision (Spring 2026)Frédo Durand, Vincent Sitzmann, Peter Holderrieth
- Video
- Panopto (public) ↗
- Homepage
- https://scenerepresentations.org/courses/2026/spring/advances-in-cv/
- Topics
- Multi-view and projective geometry · Neural scene representations · Geometric deep learning · Diffusion models · Differentiable rendering · Embodied vision for robotics
- Prerequisites
- Machine learning, linear algebra, signal processing; Python.
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CMU 17-803 Empirical Methods (Spring 2026)Bogdan Vasilescu
- Video
- YouTube (per lecture) ↗
- Homepage
- https://bvasiles.github.io/empirical-methods/
- Topics
- Research design · Interviews and qualitative coding · Survey design · Statistical modeling · Mining software repositories · Social network analysis
- Prerequisites
- Basic statistics; no programming required.
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Stanford CME296 Diffusion & Large Vision Models (Spring 2026)Afshine Amidi, Shervine Amidi
- Video
- YouTube playlist ↗
- Homepage
- https://cme296.stanford.edu/
- Topics
- Diffusion models · Score matching and flow matching · Diffusion Transformers and U-Nets · Controllable image generation · Model evaluation · Video generation
- Prerequisites
- Deep learning fundamentals; probability and linear algebra.
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MIT 6.S191 Introduction to Deep Learning (2026)Alexander Amini, Ava Amini
- Video
- YouTube playlist ↗
- Homepage
- https://introtodeeplearning.com/
- Topics
- Deep learning fundamentals · Sequence modeling · Generative modeling · Reinforcement learning · Large language models · AI for science
- Prerequisites
- Calculus and linear algebra; Python helpful.
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UMich EECS 598 Graph Algorithms via Graph Decomposition (Fall 2025)Thatchaphol Saranurak
- Video
- YouTube playlist ↗
- Homepage
- https://sites.google.com/site/thsaranurak/teaching/GraphDecomp25
- Topics
- Expander decompositions and hierarchies · Cut-matching games · Max-flow and push-relabel algorithms · Boundary-linked expander decomposition · Flow and cut sparsifiers · Dynamic shortest paths and connectivity oracles
- Prerequisites
- A first graduate algorithms course; familiarity with flows and cuts; mathematical maturity.
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MIT 6.7350 Numerical Algorithms for Computing and Machine Learning (Fall 2025)Justin Solomon
- Video
- YouTube playlist ↗
- Homepage
- https://www.youtube.com/playlist?list=PLQ3UicqQtfNsivZX5TmUAoUkkBqFT8aOL
- Topics
- Numerical linear algebra (QR, LU, SVD) · Eigenvalues and conjugate gradients · Nonlinear systems and optimization · Gradient descent and Newton methods · Interpolation and quadrature · Ordinary and partial differential equations
- Prerequisites
- Calculus, linear algebra, and programming (Python); some analysis helpful.
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ETH Computer Architecture (Fall 2025)Onur Mutlu
- Video
- YouTube playlist ↗
- Homepage
- https://safari.ethz.ch/architecture/fall2025/
- Topics
- Instruction set architecture · Pipelining and branch prediction · Caches and memory hierarchy · Virtual memory · Prefetching · Multiprocessors and accelerators
- Prerequisites
- Digital logic and a first computer-organization course.
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Stanford CME295 Transformers & Large Language Models (Autumn 2025)Afshine Amidi, Shervine Amidi
- Video
- YouTube playlist ↗
- Homepage
- https://cme295.stanford.edu/
- Topics
- Transformer architecture · LLM training and fine-tuning · Preference tuning and RLHF · Reasoning models · Retrieval-augmented generation and agents · LLM evaluation
- Prerequisites
- Neural network basics, linear algebra, probability.
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CMU 11-711 Advanced NLP (Fall 2025)Sean Welleck
- Video
- YouTube playlist ↗
- Homepage
- https://cmu-l3.github.io/anlp-fall2025/
- 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
- Prerequisites
- Machine learning and neural networks; Python.
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Yale America at 250: A History (Fall 2025)David Blight, Joanne Freeman, Beverly Gage
- Video
- YouTube playlist ↗
- 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
- Prerequisites
- None — introductory.
-
Columbia BIOL GU4310 Virology (Spring 2025)Vincent Racaniello
- Video
- YouTube playlist ↗
- Homepage
- https://www.youtube.com/playlist?list=PLGhmZX2NKiNm2iEUtVslIUHTW9i2zAG72
- Topics
- Viral structure and genomes · Replication strategies · Pathogenesis and host response · Immunity to viruses · Vaccines and antivirals · Emerging viruses
- Prerequisites
- Introductory molecular and cell biology.
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Stanford CS336 Language Modeling from Scratch (Spring 2025)Tatsunori Hashimoto, Percy Liang
- Video
- YouTube playlist ↗
- Homepage
- https://cs336.stanford.edu/
- Topics
- Tokenization · Transformer architectures · GPU kernels · Parallelism · Scaling laws · LLM evaluation
- Prerequisites
- Strong Python, deep learning, and systems programming.
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CMU 16-745 Optimal Control and Reinforcement Learning (Spring 2025)Zachary Manchester
- Video
- YouTube playlist ↗
- Homepage
- https://optimalcontrol.ri.cmu.edu/
- Topics
- LQR · Trajectory optimization · iLQR and DDP · State estimation · System identification · Reinforcement learning
- Prerequisites
- Linear algebra, calculus, dynamics; some optimization.
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MIT 18.156 Projection Theory (Spring 2025)Lawrence D. Guth
- Video
- MIT OCW ↗
- Homepage
- https://ocw.mit.edu/courses/18-156-projection-theory-spring-2025/
- Topics
- Projection theorems · Geometric measure theory · Additive combinatorics · Harmonic analysis · Homogeneous dynamics
- Prerequisites
- Graduate real analysis and measure theory; harmonic analysis helpful.
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MIT 18.100B Real Analysis (Spring 2025)Tobias Holck Colding
- Video
- MIT OCW ↗
- Homepage
- https://ocw.mit.edu/courses/18-100b-real-analysis-spring-2025/
- Topics
- Real numbers · Proof techniques · Continuity · Differentiation · Riemann integration
- Prerequisites
- Multivariable calculus; comfort writing proofs.
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Harvard CSCI E-151 Introduction to Databases with SQL (Spring 2025)Carter Zenke
- Video
- Course website ↗
- Homepage
- https://cs50.harvard.edu/extension/sql/2025/spring/
- Topics
- Relational databases · SQL querying · Schema design · Views and CTEs · Indexes · Scaling
- Prerequisites
- None — introductory; some programming helpful.
-
MIT MAS.S60 How to AI (Almost) Anything (Spring 2025)Paul Liang
- Video
- YouTube (per lecture) ↗
- Homepage
- https://mit-mi.github.io/how2ai-course/spring2025/
- Topics
- Multimodal AI · Foundation models · Medical and sensory data · Audio and video
- Prerequisites
- Machine learning and Python; deep learning helpful.
-
Harvard Law School CS50 (and AI) for Lawyers (Winter 2025)David J. Malan
- Video
- Course website ↗
- Homepage
- https://cs50.harvard.edu/hls/2025/winter/
- Topics
- Programming · Algorithms · SQL · Artificial intelligence · Web basics · Privacy and security
- Prerequisites
- None — introductory; no prior programming.
-
U of Toronto ECE454 Computer Systems Programming (Fall 2024)Jonathan Eyolfson
- Video
- YouTube playlist ↗
- Homepage
- https://eyolfson.com/courses/archive/utoronto/ece454/2024-fall/
- Topics
- Performance profiling · Compiler optimization · Memory hierarchy and caches · Dynamic memory allocation · Threading and synchronization · Rust for systems
- Prerequisites
- C, data structures, and a first systems course.
-
U of Toronto ECE344 Operating Systems (Fall 2024)Jonathan Eyolfson
- Video
- YouTube playlist ↗
- Homepage
- https://eyolfson.com/courses/archive/utoronto/ece344/2024-fall/
- Topics
- Processes and threads · Scheduling · Synchronization · Virtual memory · Filesystems · Virtualization
- Prerequisites
- C programming and computer organization.
-
MIT 21H.151 Dynastic China (Fall 2024)Tristan G. Brown
- Video
- MIT OCW / YouTube ↗
- Homepage
- https://ocw.mit.edu/courses/21h-151-dynastic-china-fall-2024/
- Topics
- Imperial Chinese state formation · Chinese political thought · Dynastic transitions · Gender and social life · Commercial history · China in global context
- Prerequisites
- None — introductory.
-
MIT 6.7960 Deep Learning (Fall 2024)Phillip Isola, Sara Beery, Jeremy Bernstein
- Video
- MIT OCW ↗
- Homepage
- https://ocw.mit.edu/courses/6-7960-deep-learning-fall-2024/
- Topics
- Neural network architectures · Learning theory · Backpropagation · Transformers · Geometry and invariances
- Prerequisites
- Machine learning, linear algebra, probability.
-
MIT 14.41 Public Finance and Public Policy (Fall 2024)Jonathan Gruber
- Video
- MIT OCW ↗
- Homepage
- https://ocw.mit.edu/courses/14-41-public-finance-and-public-policy-fall-2024/
- Topics
- Externalities · Public goods · Education policy · Health economics · Taxation · Social insurance
- Prerequisites
- Intermediate microeconomics.
-
Harvard CSCI E-80 Introduction to Artificial Intelligence with Python (Fall 2024)Brian Yu
- Video
- Course website ↗
- Homepage
- https://cs50.harvard.edu/extension/ai/2024/fall/
- Topics
- Search · Knowledge representation · Probabilistic inference · Constraint satisfaction · Neural networks · Language
- Prerequisites
- Introductory Python programming.
-
Paderborn University Reinforcement Learning (Summer 2024)Oliver Wallscheid
- Video
- YouTube playlist ↗
- Homepage
- https://github.com/upb-lea/reinforcement_learning_course_materials
- Topics
- Markov decision processes and dynamic programming · Monte Carlo and temporal-difference learning · Multi-step bootstrapping and planning · Function approximation and value-based control · Stochastic and deterministic policy gradients · Contemporary algorithms (TRPO, PPO) and safe/meta RL
- Prerequisites
- Probability, linear algebra, and Python; basic machine learning helpful.
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Stanford CS234 Reinforcement Learning (Spring 2024)Emma Brunskill
- Video
- YouTube playlist ↗
- Homepage
- https://web.stanford.edu/class/cs234/CS234Spr2024/index.html
- Topics
- Markov decision processes · Policy gradients · Q-learning · Offline RL · Exploration · Value alignment
- Prerequisites
- Machine learning, probability, Python.
-
CMU 11-785 Introduction to Deep Learning (Spring 2024)Bhiksha Raj, Rita Singh
- Video
- YouTube playlist ↗
- Homepage
- https://deeplearning.cs.cmu.edu/S24/index.html
- Topics
- MLPs · CNNs · RNNs · Attention mechanisms · Graph neural networks · Generative models
- Prerequisites
- Calculus, linear algebra, probability, Python.
-
MIT 9.35 Perception (Spring 2024)Josh McDermott
- Video
- MIT OCW / YouTube ↗
- Homepage
- https://ocw.mit.edu/courses/9-35-perception-spring-2024/
- Topics
- Auditory perception · Visual system · Psychophysics · Color and motion perception · Object recognition · Chemical senses
- Prerequisites
- Introductory psychology or neuroscience helpful.
-
Stanford CS224n Natural Language Processing with Deep Learning (Winter 2024)Christopher Manning
- Video
- YouTube playlist ↗
- Homepage
- https://web.stanford.edu/class/cs224n/
- Topics
- Word vectors · Transformers · Pre-training · Post-training · LLM agents · Benchmarking and reasoning
- Prerequisites
- Machine learning, calculus, linear algebra, Python.
-
Stanford CS236 Deep Generative Models (Fall 2023)Stefano Ermon
- Video
- YouTube playlist ↗
- Homepage
- https://deepgenerativemodels.github.io/
- Topics
- Autoregressive models · Variational autoencoders · Normalizing flows · Generative adversarial networks · Energy-based models · Score-based and diffusion models
- Prerequisites
- Machine learning, probability, and neural network basics.
-
UC Berkeley CS285 Deep Reinforcement Learning (Fall 2023)Sergey Levine
- Video
- YouTube playlist ↗
- Homepage
- https://rail.eecs.berkeley.edu/deeprlcourse/
- Topics
- Imitation learning · Policy gradients · Actor-critic methods · Model-based RL · Inverse RL · Meta-learning
- Prerequisites
- Machine learning and deep learning; probability.
-
USI Algorithmic Information Theory (Spring 2023)Charles Alexandre Bédard
- Video
- YouTube playlist ↗
- Homepage
- https://arxiv.org/abs/2504.18568
- Topics
- Computability and the universal Turing machine · Plain and prefix Kolmogorov complexity · Incompressibility and the Invariance Theorem · Solomonoff induction · Chaitin's halting probability and incompleteness · Information-theoretic limits of formal systems
- Prerequisites
- Computability and Turing machines; comfort with proofs and discrete math.
-
Stanford EE364A Convex Optimization I (2023)Stephen Boyd
- Video
- YouTube playlist ↗
- Homepage
- https://web.stanford.edu/class/ee364a/
- 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
- Prerequisites
- Linear algebra and multivariable calculus; exposure to analysis.
-
CMU 10-414/714 Deep Learning Systems (Fall 2022)Tianqi Chen, Zico Kolter
- Video
- YouTube (per lecture) ↗
- Homepage
- https://dlsyscourse.org/
- Topics
- Automatic differentiation · GPU computation · Neural network compilers · Operator fusion · Backpropagation implementation
- Prerequisites
- Deep learning basics and strong Python/C++.
-
Freiburg Quantum Information Theory (Summer 2022)Christoph Dittel, Andreas Buchleitner
- Video
- YouTube playlist ↗
- Homepage
- https://arxiv.org/abs/2311.12442
- Topics
- Qubits and quantum states · Quantum entanglement and measurement · Quantum gates and circuits · Quantum algorithms · Quantum channels and decoherence · Quantum error correction
- Prerequisites
- Linear algebra over complex vector spaces; basic quantum mechanics helpful.
-
UC Irvine High-Dimensional Probability (2022)Roman Vershynin
- Video
- YouTube playlist ↗
- Homepage
- https://www.math.uci.edu/~rvershyn/teaching/hdp/hdp.html
- 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
- Prerequisites
- Measure-theoretic or strong undergraduate probability; linear algebra; mathematical maturity.
-
Stanford CS229M / STATS214 Machine Learning Theory (Fall 2021)Tengyu Ma
- Video
- YouTube playlist ↗
- Homepage
- https://web.stanford.edu/class/stats214/
- 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
- Prerequisites
- Probability, linear algebra, machine learning; mathematical maturity.
-
NYU DS-GA 1008 Deep Learning (Spring 2021)Yann LeCun, Alfredo Canziani
- Video
- YouTube playlist ↗
- Homepage
- https://atcold.github.io/NYU-DLSP21/
- 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
- Prerequisites
- Machine learning, linear algebra, calculus, Python.
-
UC Berkeley CS182 Deep Neural Networks (Spring 2021)Sergey Levine
- Video
- YouTube playlist ↗
- Homepage
- https://cs182sp21.github.io/
- Topics
- Backpropagation · CNNs · RNNs · Transformers · Meta-learning · Generative models
- Prerequisites
- Machine learning basics; calculus, linear algebra, Python.
-
MIT 14.13 Psychology and Economics (Spring 2020)Frank Schilbach
- Video
- MIT OCW ↗
- Homepage
- https://ocw.mit.edu/courses/14-13-psychology-and-economics-spring-2020/
- Topics
- Time preferences and self-control · Risk preferences · Social preferences and reciprocity · Limited attention · Default effects and nudges · Poverty and psychology
- Prerequisites
- Introductory microeconomics.
-
UMich EECS 498-007 / 598-005 Deep Learning for Computer Vision (Fall 2019)Justin Johnson
- Video
- YouTube playlist ↗
- Homepage
- https://web.eecs.umich.edu/~justincj/teaching/eecs498/FA2019/
- Topics
- Linear classifiers and backpropagation · Convolutional and recurrent networks · Attention and transformers · Object detection and segmentation · Generative models (GANs, VAEs) · Deep reinforcement learning
- Prerequisites
- Linear algebra, calculus, Python; basic machine learning helpful.
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MIT 9.13 The Human Brain (Spring 2019)Nancy Kanwisher
- Video
- MIT OCW / YouTube ↗
- Homepage
- https://ocw.mit.edu/courses/9-13-the-human-brain-spring-2019/
- 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
- Prerequisites
- Introductory biology or psychology helpful; none required.
-
CMU 15-855 Graduate Computational Complexity Theory (Fall 2017)Ryan O'Donnell
- Video
- YouTube playlist ↗
- Homepage
- http://www.cs.cmu.edu/~odonnell/complexity17/
- Topics
- Time and space hierarchy theorems · Circuit complexity · Randomized complexity · Interactive proofs · PCP theorem · Hardness amplification
- Prerequisites
- Undergraduate theory of computation; mathematical maturity.
-
MIT 18.650 Statistics for Applications (Fall 2016)Philippe Rigollet
- Video
- MIT OCW / YouTube ↗
- Homepage
- https://ocw.mit.edu/courses/18-650-statistics-for-applications-fall-2016/
- 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
- Prerequisites
- Probability and calculus; linear algebra helpful.
-
UCL / DeepMind Introduction to Reinforcement Learning (2015)David Silver
- Video
- YouTube playlist ↗
- Homepage
- https://davidstarsilver.wordpress.com/teaching/
- Topics
- Markov decision processes · Dynamic programming · Monte Carlo and TD methods · Value function approximation · Policy gradients · Exploration and integration with planning
- Prerequisites
- Probability and basic machine learning; calculus.
-
Cornell MAE5790 Nonlinear Dynamics and Chaos (Spring 2014)Steven Strogatz
- Video
- YouTube playlist ↗
- Homepage
- https://www.stevenstrogatz.com/teaching
- Topics
- Phase plane analysis · Bifurcations · Limit cycles · Lorenz equations · Chaos and strange attractors · Fractals
- Prerequisites
- Differential equations and multivariable calculus.
-
NEW Harvard STAT 110 Probability (Fall 2013)Joe Blitzstein
- Video
- YouTube playlist ↗
- Homepage
- https://stat110.hsites.harvard.edu/
- Topics
- Probability axioms and combinatorics · Conditional probability and Bayes' rule · Random variables and named distributions · Expectation, variance, and moments · Joint, marginal, and conditional distributions · Markov chains and limit theorems
- Prerequisites
- Single-variable calculus; comfort with basic proofs.
-
Stanford CS364A Algorithmic Game Theory (Fall 2013)Tim Roughgarden
- Video
- YouTube (per lecture) ↗
- Homepage
- https://timroughgarden.org/f13/f13.html
- Topics
- Mechanism design · Vickrey and Myerson auctions · Price of anarchy · Selfish routing · No-regret learning · Nash equilibrium complexity
- Prerequisites
- Algorithms and discrete math; mathematical maturity.
-
Caltech CS156 Learning From Data (2012)Yaser Abu-Mostafa
- Video
- YouTube / course website ↗
- Homepage
- https://work.caltech.edu/telecourse.html
- 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
- Prerequisites
- Probability, linear algebra, basic calculus.
-
MIT 6.262 Discrete Stochastic Processes (Spring 2011)Robert Gallager
- Video
- MIT OCW / YouTube ↗
- Homepage
- https://ocw.mit.edu/courses/6-262-discrete-stochastic-processes-spring-2011/
- Topics
- Poisson and renewal processes · Markov chains: finite and countable state · Random walks and martingales · Markov processes in continuous time · Queueing and large deviations
- Prerequisites
- Undergraduate probability; comfort with proofs.
-
MIT 6.034 Artificial Intelligence (Fall 2010)Patrick Winston
- Video
- MIT OCW / YouTube ↗
- Homepage
- https://ocw.mit.edu/courses/6-034-artificial-intelligence-fall-2010/
- 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
- Prerequisites
- Programming experience; data structures helpful.
-
Stanford BIO 150 Human Behavioral Biology (Spring 2010)Robert Sapolsky
- Video
- YouTube playlist ↗
- Homepage
- https://www.youtube.com/playlist?list=PL848F2368C90DDC3D
- Topics
- Behavioral evolution · Molecular genetics and heritability · Ethology and neuroscience · Aggression and cooperation · Sexual behavior · Individual differences and psychiatric disorders
- Prerequisites
- None — introductory.
-
MIT 18.06 Linear Algebra (Spring 2010)Gilbert Strang
- Video
- MIT OCW / YouTube ↗
- Homepage
- https://ocw.mit.edu/courses/18-06-linear-algebra-spring-2010/
- Topics
- Systems of equations and elimination · Vector spaces and subspaces · Orthogonality and least squares · Determinants · Eigenvalues and diagonalization · Positive definite and singular value decomposition
- Prerequisites
- Single-variable calculus.
-
Harvard Justice — What's the Right Thing to Do? (Fall 2009)Michael Sandel
- Video
- YouTube playlist ↗
- Homepage
- https://justiceharvard.org/
- Topics
- Utilitarianism · Libertarianism · Kantian ethics · Rawls and distributive justice · Affirmative action and markets · Citizenship and the common good
- Prerequisites
- None — introductory.
-
Yale E&EB 122 Principles of Evolution, Ecology and Behavior (Spring 2009)Stephen C. Stearns
- Video
- Open Yale Courses (video) ↗
- Homepage
- https://oyc.yale.edu/ecology-and-evolutionary-biology/eeb-122
- Topics
- Natural selection and genetic drift · Life history evolution · Sexual selection and mating systems · Speciation and phylogenetics · Evolutionary medicine · Ecology and biodiversity
- Prerequisites
- None — introductory.
-
Yale ECON 252 Financial Markets (Spring 2008)Robert J. Shiller
- Video
- Open Yale Courses (video) ↗
- Homepage
- https://oyc.yale.edu/economics/econ-252-08
- Topics
- Risk and portfolio diversification · Behavioral finance · Debt and equity markets · Insurance and banking · Real estate finance · Derivatives and regulation
- Prerequisites
- Introductory economics; basic probability helpful.
-
Yale ECON159 Game Theory (Fall 2007)Ben Polak
- Video
- Open Yale Courses (video) ↗
- Homepage
- https://oyc.yale.edu/economics/econ-159
- Topics
- Dominance · Nash equilibrium · Backward induction · Evolutionary stability · Asymmetric information · Auctions
- Prerequisites
- Introductory economics; basic calculus helpful.
-
Yale PSYC 110 Introduction to Psychology (Spring 2007)Paul Bloom
- Video
- Open Yale Courses (video) ↗
- Homepage
- https://oyc.yale.edu/psychology/psyc-110
- Topics
- Neuroscience and the brain · Perception and learning · Memory and cognition · Development and language · Social psychology · Mental illness and happiness
- Prerequisites
- None — introductory.
-
Yale PHIL 176 Death (Spring 2007)Shelly Kagan
- Video
- Open Yale Courses (video) ↗
- Homepage
- https://oyc.yale.edu/philosophy/phil-176
- Topics
- Dualism vs. physicalism · Personal identity · The nature of death · Whether death is bad · Immortality and the value of life · Suicide and rationality
- Prerequisites
- None — introductory.
-
MIT 6.001 Structure and Interpretation of Computer Programs (1986)Harold Abelson, Gerald Jay Sussman
- Video
- MIT OCW (1986 HP video) ↗
- Homepage
- https://groups.csail.mit.edu/mac/classes/6.001/abelson-sussman-lectures/
- 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
- Prerequisites
- None — introductory; some prior programming exposure helps.
-
Cornell CS 6120 Advanced Compilers (Self-Guided)Adrian Sampson
- Video
- Cornell video-on-demand ↗
- Homepage
- https://www.cs.cornell.edu/courses/cs6120/2025fa/self-guided/
- Topics
- Intermediate representations and SSA · Data-flow analysis · LLVM · Loop and interprocedural optimization · Garbage collection and JIT compilation · Concurrency and parallelism
- Prerequisites
- An undergraduate compilers course or strong systems programming; data structures.
-
Santa Fe Institute Introduction to ComplexityMelanie Mitchell
- Video
- Complexity Explorer (free registration) ↗
- Homepage
- https://www.complexityexplorer.org/courses/165-introduction-to-complexity
- Topics
- Dynamics and chaos · Fractals · Information theory and entropy · Cellular automata · Genetic algorithms · Networks and self-organization
- Prerequisites
- None — introductory; basic algebra.
-
Yale Reading Marx's Capital, Volume 1Paul North
- Video
- YouTube playlist ↗
- Topics
- Political economy · Capital and labor · Value and commodities · Class struggle · Historical materialism · Accumulation
- Prerequisites
- None — introductory; willingness to read closely.
No courses match these filters.