apps.ofthevalley.cclogin
Open Courses
University lecture series, newest first
  • 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.
  • 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.
  • 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.
  • 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.
  • Video
    YouTube playlist ↗
    Homepage
    https://missing.csail.mit.edu/2026
    Topics
    The shell and command-line environment · Development environment and tools · Debugging and profiling · Version control with Git · Packaging and shipping code · Agentic coding and code quality
    Prerequisites
    None — introductory; some prior programming exposure helps.
  • 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.
  • Video
    YouTube playlist ↗
    Homepage
    https://rdi.berkeley.edu/agentic-ai/f25
    Topics
    LLM reasoning, planning, and agent frameworks · Post-training and verifiable agents · Agent evaluation and benchmarking · Multi-agent systems · Agents for scientific discovery and robotics · Agentic AI safety and security
    Prerequisites
    Machine learning and deep learning; familiarity with large language models.
  • Video
    YouTube playlist ↗
    Homepage
    https://www.cs.sjsu.edu/~stamp/infosec/
    Topics
    Symmetric and public-key cryptography · Hash functions and cryptanalysis · Authentication and access control · Security protocols (SSL, IPsec, Kerberos) · Software flaws, malware, and buffer overflows · Operating system security
    Prerequisites
    Undergraduate computer science; some discrete math and programming.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • Video
    YouTube playlist ↗
    Homepage
    https://uni-tuebingen.de/fakultaeten/mathematisch-naturwissenschaftliche-fakultaet/fachbereiche/informatik/lehrstuehle/autonomous-vision/lectures/computer-vision/
    Topics
    Image formation and camera models · Structure-from-motion and bundle adjustment · Stereo and multi-view 3D reconstruction · Probabilistic graphical models · Coordinate-based networks and NeRF · Recognition, segmentation, and self-supervised learning
    Prerequisites
    Linear algebra, probability, and machine-learning basics.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • Video
    YouTube playlist ↗
    Homepage
    https://diffusion.csail.mit.edu/2025/
    Topics
    Flow and diffusion models · Flow matching and ODEs/SDEs · Score functions and score matching · Classifier-free guidance · Latent spaces and network architectures · Generative robotics
    Prerequisites
    Probability, linear algebra, and differential equations; deep learning fundamentals; Python.
  • 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.
  • Video
    YouTube playlist ↗
    Homepage
    https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/index-eng.html
    Topics
    Linear and logistic regression · Resampling methods and the bias-variance tradeoff · Neural networks and deep learning · Decision trees, random forests, and boosting · Support vector machines and kernels · Unsupervised learning: PCA and clustering
    Prerequisites
    Linear algebra, calculus, and probability/statistics; Python programming.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • Video
    YouTube playlist ↗
    Homepage
    https://ppc.cs.aalto.fi/
    Topics
    Instruction-level parallelism and vectorization · Memory hierarchy and cache optimization · Multithreading with OpenMP · GPU programming with CUDA · Algorithm engineering for performance · Case study: optimizing from baseline to tuned code
    Prerequisites
    C++ programming; basic algorithms and data structures.
  • Video
    MIT OCW / YouTube ↗
    Homepage
    https://ocw.mit.edu/courses/2-43-advanced-thermodynamics-spring-2024/
    Topics
    First law, energy, and equilibrium states · Second law, entropy, and the maximum entropy principle · Adiabatic availability and exergy · Gibbs paradox and Shannon information entropy · Quantum foundations of thermodynamics · Multicomponent and thermodiffusive transport
    Prerequisites
    Undergraduate thermodynamics; multivariable calculus; some quantum mechanics helpful.
  • 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.
  • 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.
  • 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.
  • 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.
  • Video
    YouTube playlist ↗
    Homepage
    https://www.youtube.com/playlist?list=PL_xRyXins84_Jf-aCh7chj47HR4oZLPwK
    Topics
    Entity-Component-System game engine architecture · C++ and the SFML graphics library · Vector math and 2D game physics · Collision detection · Rendering and animation · Game AI and pathfinding
    Prerequisites
    Intermediate programming and data structures; some C++ helpful.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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++.
  • Video
    YouTube playlist ↗
    Homepage
    https://www.youtube.com/playlist?list=PLaMjLYzDGxvz1oT5gpFiY6rJZnlJ-1Xu-
    Topics
    Foundations and history of cognitive science · Perception and attention · Memory and learning · Language and concepts · Reasoning and decision-making · Consciousness and the mind–brain relationship
    Prerequisites
    None — introductory.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • Video
    YouTube playlist ↗
    Homepage
    https://kuleshov-group.github.io/aml-website/
    Topics
    Supervised learning and linear models · Optimization and gradient descent · Support vector machines and kernels · Tree-based methods and boosting · Unsupervised learning and dimensionality reduction · Model evaluation and the ML development workflow
    Prerequisites
    Linear algebra, probability, and Python; basic calculus.
  • 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.
  • Video
    YouTube playlist ↗
    Homepage
    https://jukkasuomela.fi/da2020/
    Topics
    Port-numbering, LOCAL, and CONGEST models · Graph-theoretic foundations · Randomized distributed algorithms · Covering maps and local neighborhoods · Round elimination and sinkless orientation · Lower bounds and hardness of coloring
    Prerequisites
    Discrete math and graph theory; algorithms, data structures, and models of computation.
  • Video
    YouTube playlist ↗
    Homepage
    https://www.cs.virginia.edu/~robins/cs6161/
    Topics
    Problem solving, proofs, and the halting problem · Asymptotics, recurrences, and data structures · Sorting, selection, and order statistics · Computational geometry: convex hulls and Voronoi diagrams · NP-completeness and approximation algorithms · Computational universality and the Church-Turing thesis
    Prerequisites
    Undergraduate algorithms and data structures; discrete math and mathematical maturity.
  • 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.
  • 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.
  • Video
    YouTube playlist ↗
    Homepage
    https://cbmm.mit.edu/education/courses/statistical-learning-theory-and-applications
    Topics
    Regularization and reproducing kernel Hilbert spaces · Generalization and stability · VC dimension and complexity of function spaces · Support vector machines and kernel methods · Deep networks: approximation and optimization · Theory of learning and representation
    Prerequisites
    Multivariable calculus and linear algebra; probability and functional analysis helpful; mathematical maturity.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • Video
    YouTube playlist ↗
    Homepage
    https://www.youtube.com/playlist?list=PLbyW0t9gkXg0NtX6IYCwQjxDD8yvcS1pX
    Topics
    O-notation and complexity analysis · Sorting: merge sort, quicksort, heapsort, linear-time sorting · Dynamic programming, divide and conquer, greedy algorithms · Trees: binary search trees, red-black trees, B-trees · Hash tables · Priority queues and heaps
    Prerequisites
    None — introductory; basic programming and discrete math.
  • Video
    YouTube playlist ↗
    Homepage
    https://www.santoshvenkatesh.com/video-lectures
    Topics
    Combinatorial counting methods · Probability spaces and axioms · Conditional probability and independence · Discrete and continuous distributions · Binomial and Poisson models · Laws of large numbers and the central limit theorem
    Prerequisites
    Single-variable calculus.
  • Video
    YouTube playlist ↗
    Homepage
    https://mathweb.ucsd.edu/~tkemp/ProbabilityTube/
    Topics
    Measure-theoretic foundations of probability · Independence and the strong law of large numbers · Weak convergence, characteristic functions, and the CLT · Conditional expectation and martingales · Markov chains and Markov processes · Brownian motion
    Prerequisites
    Real analysis and measure theory; strong undergraduate probability; mathematical maturity.
  • 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.
  • 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.
  • Video
    YouTube playlist ↗
    Topics
    Political economy · Capital and labor · Value and commodities · Class struggle · Historical materialism · Accumulation
    Prerequisites
    None — introductory; willingness to read closely.