└── README.md /README.md: -------------------------------------------------------------------------------- 1 | # Mathematics / Computer Science Courses 2 | A collection of some useful mathematics and computer science courses 3 | 4 | 5 | ## Courses 6 | 7 | ### Deep Learning for Computer Vision 8 | Prof. Justin Johnson, University of Michigan, 2019 9 | 10 | ![image](https://github.com/user-attachments/assets/371501d1-21d4-4b2a-bba9-c8e51d2dd62a) 11 | 12 | - Linear classifiers 13 | - Stochastic gradient descent 14 | - Fully-connected networks 15 | - Convolutional networks 16 | - Recurrent networks 17 | - Attention and transformers 18 | - Object detection 19 | - Image segmentation 20 | - Video classification 21 | - Generative models (GANs, VAEs, autoregressive models) 22 | - Reinforcement Learning 23 | 24 | πŸŽ₯ Lectures: https://youtube.com/playlist?list=PL5-TkQAfAZFbzxjBHtzdVCWE0Zbhomg7r 25 | 26 | 27 | ### High-Dimensional Probability 28 | Roman Vershynin 29 | 30 | ![image](https://github.com/user-attachments/assets/37f50b44-67e2-4136-abb8-a74857ca56ae) 31 | 32 | πŸŽ₯ Lectures: https://youtube.com/playlist?list=PLPjEEUWIWhQV7X6dXfrVP3w0KBBLBVJ0j 33 | πŸ“” Textbook : https://www.math.uci.edu/~rvershyn/papers/HDP-book/HDP-book.html 34 | 35 | 36 | ### Deep Unsupervised Learning 37 | Prof. Pieter Abbeel, UC Berkeley, 2024 38 | 39 | ![image](https://github.com/user-attachments/assets/eb151b94-f8fe-4bb5-8d0d-28525eee6532) 40 | 41 | - Autoregressive Models 42 | - Flow Models 43 | - Latent Variable Models & Variational AutoEncoders (VAEs) 44 | - Generative Adversarial Networks (GANs) 45 | - Diffusion Models 46 | - Self-Supervised Learning 47 | - Large Language Models (LLMs) 48 | - Generative Video 49 | - Semisupervised Learning & Unsupervised Distribution Alignment 50 | - Generative Modeling for Science 51 | - Neural Radiance Fields 52 | - Multimodal Models 53 | - Parallelization 54 | 55 | πŸŽ₯ Lectures: https://youtube.com/playlist?list=PLwRJQ4m4UJjPIvv4kgBkvu_uygrV3ut_U 56 | 57 | 58 | ### Deep Generative Models 59 | Prof. Stefano Ermon, Stanford University, 2023 60 | 61 | ![image](https://github.com/user-attachments/assets/ead0b70a-a22f-4c6b-b008-45c961b0b078) 62 | 63 | - Autoregressive Models 64 | - Maximum Likelihood Learning 65 | - Variational AutoEncoders (VAEs) 66 | - Normalizing Flows 67 | - Generative Adversarial Networks (GANs) 68 | - Energy Based Models (EBMs) 69 | - Score Based Models 70 | - Evaluation of Generative Models 71 | 72 | πŸŽ₯ Lectures: https://youtube.com/playlist?list=PLoROMvodv4rPOWA-omMM6STXaWW4FvJT8 73 | πŸ“” Course page containing lecture notes: https://deepgenerativemodels.github.io 74 | 75 | 76 | ### Geometric Deep Learning 77 | African Master’s in Machine Intelligence, 2022 78 | 79 | ![image](https://github.com/user-attachments/assets/105b669d-d05d-4742-b1c6-1b6ecf7c274e) 80 | 81 | - High-Dimensional Learning 82 | - Geometric Priors 83 | - Graphs & Sets 84 | - Grids 85 | - Groups 86 | - Geodesics & Manifolds 87 | - Gauges 88 | 89 | πŸŽ₯ Lectures: https://youtube.com/playlist?list=PLn2-dEmQeTfSLXW8yXP4q_Ii58wFdxb3C 90 | πŸ“” webpage : https://geometricdeeplearning.com 91 | 92 | 93 | ### Machine Learning with Graphs 94 | Prof. Jure Leskovec, Stanford University, 2021 95 | 96 | ![image](https://github.com/user-attachments/assets/bc56580c-b0a2-459f-846a-0d6f40ac2f74) 97 | 98 | πŸŽ₯ Lectures: https://youtube.com/playlist?list=PLoROMvodv4rPLKxIpqhjhPgdQy7imNkDn 99 | 100 | 101 | ### Reinforcement Learning Theory 102 | Prof. Csaba SzepesvΓ‘ri, University of Alberta, 2022 103 | 104 | ![image](https://github.com/user-attachments/assets/ec632aea-95ad-4077-ae44-796af53b9326) 105 | 106 | - MDP, Fundamental Theorem 107 | - Value and Policy Iteration 108 | - Local Planning 109 | - Function Approximation 110 | - Approximate Policy Iteration 111 | - Planning Complexity, TensorPlan 112 | - Lower Bound for API and POLITEX 113 | - Policy Search 114 | - Batch RL 115 | - Online RL 116 | 117 | πŸŽ₯ Lectures: [https://www.youtube.com/playlist?list=PLQCZ7_TRKVIzODPXorEyvhCk25TlcTANC 118 | ](https://www.youtube.com/playlist?list=PLQCZ7_TRKVIzODPXorEyvhCk25TlcTANC) 119 | πŸ“” webpage : https://rltheory.github.io/ 120 | 121 | 122 | ### Multi-Agent Reinforcement Learning 123 | Dr. Stefano V. Albrecht, 2023 124 | ![image](https://github.com/user-attachments/assets/465dd8a7-25c2-4f5b-b95d-a6a0f934587b) 125 | 126 | πŸŽ₯ Lectures: [https://www.youtube.com/playlist?list=PLkoCa1tf0XjCU6GkAfRCkChOOSH6-JC_2](https://www.youtube.com/playlist?list=PLkoCa1tf0XjCU6GkAfRCkChOOSH6-JC_2) 127 | πŸ“” Textbook : http://www.marl-book.com/ 128 | 129 | 130 | ### Numerics of Machine Learning 131 | University of TΓΌbingen, 2023 132 | 133 | ![image](https://github.com/user-attachments/assets/8dce5eb8-5d27-47b0-9cbb-e5def426f103) 134 | 135 | 136 | - Numerical Linear Algebra 137 | - Scaling Gaussian Processes 138 | - Computation-aware Gaussian Processes 139 | - State Space Models 140 | - Solving Ordinary Differential Equations 141 | - Probabilistic Numerical ODE Solvers 142 | - Partial Differential Equations 143 | - Monte Carlo 144 | - Bayesian Quadrature 145 | - Optimization for Deep Learning 146 | - Second-order Optimization for Deep Learning 147 | - Uncertainty in Deep Learning 148 | 149 | πŸŽ₯ Lectures : https://www.youtube.com/playlist?list=PL05umP7R6ij2lwDdj7IkuHoP9vHlEcH0s 150 | 151 | 152 | ### Reinforcement Learning 153 | Stanford, Prof. Emma Brunskill, 2024 154 | 155 | ![image](https://github.com/user-attachments/assets/96785d68-6730-49d0-9d4a-b91a2009ebac) 156 | 157 | - Introduction to Reinforcement Learning 158 | - Tabular MDP planning 159 | - Policy Evaluation 160 | - Q learning and Function Approximation 161 | - Policy Search 162 | - Offline RL 163 | - Exploration 164 | - Multi-Agent Games 165 | - Value Alignment 166 | 167 | 168 | πŸŽ₯ Lectures: https://www.youtube.com/playlist?list=PLoROMvodv4rN4wG6Nk6sNpTEbuOSosZdX 169 | 170 | 171 | ### Model-Based Image and Signal Processing 172 | Purdue University, Prof. Charles A. Bouman, 2020 173 | 174 | ![image](https://github.com/user-attachments/assets/2a506822-0dba-4f8d-9c2f-994913b6111f) 175 | 176 | - Probability 177 | - Causal Gaussian Models 178 | - Non-Causal Gaussian Models 179 | - MAP with Gaussian Priors 180 | - Non-Gaussian Markov Random Fields 181 | - Non-Gaussian MAP 182 | - Majorization 183 | - Constrained Optimization 184 | - Plug and Play 185 | - EM Algorithm 186 | - Markov Chains and HMMs 187 | - General MRFs 188 | - Stochastic Simulation 189 | - MAP Segmentation 190 | 191 | πŸŽ₯ Lectures : https://www.youtube.com/playlist?list=PL3ZrjaBngMS0mTSoMsy7P6rTFSgsmsMw3 192 | πŸ“” Textbook: https://engineering.purdue.edu/~bouman/publications/FCI-book/ 193 | 194 | 195 | 196 | ### Convex Optimization 197 | Stanford, Prof. Stephen Boyd, 2023 198 | 199 | ![image](https://github.com/user-attachments/assets/a30ba279-6fd2-4045-893b-b1535c1e0bdb) 200 | 201 | - Convex sets 202 | - Convex functions 203 | - Convex optimization problems 204 | - Duality 205 | - Approximation and fitting 206 | - Statistical estimation 207 | - Geometric problems 208 | - Numerical linear algebra background 209 | - Unconstrained minimization 210 | - Equality constrained minimization 211 | - Interior-point methods 212 | 213 | πŸŽ₯ Lectures: https://www.youtube.com/playlist?list=PLoROMvodv4rMJqxxviPa4AmDClvcbHi6h 214 | πŸ“” Textbook : https://stanford.edu/~boyd/cvxbook/ 215 | 216 | 217 | ### Learning in Games and Games in Learning 218 | University of Pennsylvania, Prof. Aaron Roth, 2023 219 | 220 | ![image](https://github.com/user-attachments/assets/a1d55878-2c1a-4ccc-9e89-744c92fd7521) 221 | A mathematical course focusing on the interplay between game theory and machine learning: 222 | - Introduction to sequential learning 223 | - Halving algorithm 224 | - Follow the perturbed leader 225 | - Follow the regularized leader 226 | - Online convex optimization 227 | - Zero-sum games, Minimax theorem 228 | - Deriving a no regret learning algorithm 229 | - Correlated equilibrium, Swap regret 230 | - The adversary moves first framework 231 | - Multi-objective sequential learning 232 | 233 | πŸŽ₯ YouTube (24 lectures) : https://www.youtube.com/playlist?list=PLlIlhe_rS4U2p_fxYwB15nhzHEKF53xUl 234 | πŸ“” Lecture notes: https://mlgametheory.com/ 235 | 236 | 237 | ### Foundations of Reinforcement Learning 238 | Princeton University, Prof. Chi Jin, 2024 239 | 240 | ![image](https://github.com/user-attachments/assets/7e879ebd-e311-48b7-bb76-7b6c42da3ae9) 241 | A graduate level course on theoretical foundations of reinforcement learning: 242 | - MDP basics and planning 243 | - Concentration inequalities, Martingale concentrations 244 | - Generative models, value iteration 245 | - Online RL, exploration, optimism 246 | - Minimax lower bound 247 | - Offline RL, pessimism 248 | - Policy optimization 249 | - Large state space, linear function approximation 250 | - General function approximation 251 | - Game theory and multiagent RL 252 | - Learning Markov games 253 | - Partial observable MDP 254 | 255 | 256 | πŸŽ₯ YouTube (22 lectures) : https://www.youtube.com/playlist?list=PLYXvCE1En13epbogBmgafC_Yyyk9oQogl 257 | πŸ“” Course page containing lecture notes: https://sites.google.com/view/cjin/teaching/ece524 258 | 259 | 260 | 261 | ### Graduate Topics in Deep Learning Theory 262 | Harvard Center of Mathematical Sciences and Applications, Dr. Eli Grigsby, 2024 263 | 264 | ![image](https://github.com/user-attachments/assets/e8dba6f0-7dab-443f-9fa6-033b42e8ed8c) 265 | 266 | A course on geometric aspects of deep learning theory: 267 | - The geometry and combinatorics of feedforward ReLU neural networks as piecewise linear function classes 268 | - Neural networks as universal approximators: discrete and non-discrete versions 269 | - The role of the superposition hypothesis in mechanistic interpretability of neural networks 270 | - Neural network architectures for sequence-to-sequence processing 271 | - Representing finite state automata using sequence-to-sequence architectures 272 | - Geometric distortion in deep networks and the importance of residual connections 273 | - Symmetries of overparameterized ReLU neural networks, optimization, and generalization 274 | - Algorithmic computation of topological invariants of decision boundaries/regions 275 | 276 | πŸŽ₯ YouTube: https://youtube.com/playlist?list=PL0NRmB0fnLJSEXFQHGF0q5JcedxTqK4AJ&si=G0rk4GBgywt6kypK 277 | πŸ“” Course page : https://sites.google.com/bc.edu/eli-grigsby/mt875-mechanistic-interpretability 278 | 279 | 280 | ### Probabilistic Programming 281 | University of British Columbia, Dr. Frank Wood, 2021 282 | 283 | ![image](https://github.com/user-attachments/assets/8293bbb3-42a2-4a9b-9288-fe5941422aa3) 284 | 285 | 286 | - Introduction to Model-Based Reasoning 287 | - Graphical Models 288 | - Inference, Learning, Monte Carlo, Sampling 289 | - Markov Chain Monte Carlo 290 | - First Order Probabilistic Programming Languages 291 | - Graphical Model Compilation 292 | - Graph-Based Inference 293 | - Hamiltonian Monte Carlo 294 | - Evaluation-based Inference 295 | - Variational Inference 296 | - Higher Order Probabilistic Programming Languages 297 | - Amortized Inference / Guide Programs / Inference Compilation 298 | - Reinforcement Learning as Inference 299 | - Alternative Variational Bounds 300 | - Reparametrization and Normalizing Flows 301 | 302 | πŸŽ₯ 25 lectures on YouTube: https://youtube.com/playlist?list=PLRBUAK6di_6XlF7KAZBPRgcP0zD5sVXcN&si=9hjsRE1bav7vTqbG 303 | πŸ“” An Introduction to Probabilistic Programming: https://arxiv.org/abs/1809.10756 304 | 305 | 306 | ### Learning and Reasoning with Bayesian Networks 307 | UCLA, Prof. Adnan Darwiche 308 | 309 | ![image](https://github.com/user-attachments/assets/cfac9ac2-0711-4a78-b87c-7f0e0d258cd1) 310 | 311 | 312 | - Propositional Logic 313 | - Probability Calculus: Beliefs and Hard Evidence, Soft Evidence 314 | - Bayesian Networks: Syntax and Semantics 315 | - Bayesian Networks: Independence and d-Separation 316 | - Probabilistic Queries and their Complexity 317 | - Building Bayesian Networks 318 | - Inference by Variable Elimination 319 | - The Jointree Algorithm 320 | - Inference by Conditioning 321 | - Arithmetic Circuits 322 | - Loopy Belief Propagation 323 | - Learning Parameters 324 | - Learning Network Structure 325 | - Bayesian Learning 326 | - Causality 327 | - Sensitivity Analysis 328 | - Reasoning about Classifiers 329 | - Explaining Classifiers 330 | 331 | 332 | πŸŽ₯ YouTube Playlist(32 lectures + 4 additional lectures on causality): https://youtube.com/playlist?list=PLlDG_zCuBub6ywAIrM1DfJp8xaeVjyvwx 333 | πŸ“” Textbook: Modeling and Reasoning with Bayesian Networks, Adnan Darwiche 334 | 335 | 336 | ### Kernel methods in machine learning 337 | ENS Paris-Saclay, Dr. Julien Mairal, Dr. Jean-Philippe Vert 338 | 339 | ![image](https://github.com/user-attachments/assets/74964615-14cf-4d76-9771-563c24383417) 340 | 341 | 342 | - Positive definite kernels 343 | - Reproducing Kernel Hilbert Space 344 | - Smoothness functional, Kernel trick, Representer theorem 345 | - Kernel ridge and logistic regression 346 | - Large-margin classifiers, SVMs 347 | - Unsupervised kernel methods 348 | - Green, Mercer, Herglotz, Bochner and friends 349 | - Kernels for graphs 350 | - Multiple kernels learning 351 | - Large-scale learning 352 | - Deep kernel machines 353 | - Kernels for probabilistic models 354 | - Kernel mean embedding 355 | - Characteristic kernels 356 | 357 | 358 | πŸŽ₯ YouTube Playlist (25 lectures): https://www.youtube.com/playlist?list=PLD93kGj6_EdrkNj27AZMecbRlQ1SMkp_o 359 | 360 | 361 | ### Advanced Robotics 362 | 363 | UC Berkeley, Prof. Pieter Abbeel, 2019 364 | 365 | ![image](https://github.com/user-attachments/assets/dbc60104-f42a-47d9-9dd9-cac6a2b61316) 366 | 367 | - Markov Decision Processes: Exact Methods 368 | - Discretization of Continuous State Space MDPs 369 | - Function Approximation 370 | - LQR, iterative LQR, Differential Dynamic Programming 371 | - Unconstrained Optimization 372 | - Constrained Optimization 373 | - Optimization-based Control 374 | - Motion Planning 375 | - Kalman Filtering, EKF, UKF 376 | - Smoother, MAP, Maximum Likelihood, EM, KF parameter estimation 377 | - Particle Filters 378 | - Partially Observable MDPs 379 | - Imitation Learning 380 | - RL : Policy Gradients, Off-policy RL, Model-based RL 381 | - Physics Simulation 382 | 383 | πŸŽ₯ YouTube Playlist (24 lectures) : https://youtube.com/playlist?list=PLwRJQ4m4UJjNBPJdt8WamRAt4XKc639wF&si=LrZXaiXafs6Qj07x 384 | 385 | 386 | 387 | ### Statistical Machine Learning 388 | Carnegie Mellon University, Prof. Larry Wasserman, 2016 389 | 390 | ![image](https://github.com/user-attachments/assets/ba4ba579-a388-4a0f-b0d7-55f5946df30c) 391 | 392 | 393 | - Function Spaces 394 | - Concentration of Measure 395 | - Linear Regression 396 | - Non-Parametric Regression 397 | - Trend Filtering 398 | - Linear Classification 399 | - Non-Parametric Classification 400 | - Minimax Theory 401 | - Non-Parametric Bayes 402 | - Boosting 403 | - Clustering 404 | - Graphical Models 405 | - Dimension Reduction 406 | - Random Matrix Theory 407 | - Differential Privacy 408 | 409 | πŸŽ₯ YouTube Playlist (24 lectures) : https://youtube.com/playlist?list=PLTB9VQq8WiaCBK2XrtYn5t9uuPdsNm7YE&si=T5N31V-7ZPA_onXN 410 | 411 | 412 | 413 | 414 | ### Optimization Methods for Machine Learning and Engineering 415 | KIT(2020), Dr. Julius Pfrommer 416 | 417 | ![image](https://github.com/user-attachments/assets/d644a17b-ffd6-49f5-a416-a970a68a27ff) 418 | 419 | - Introduction, Convexity and Gradient Descent 420 | - Newton’s Method 421 | - Inequality Constrained Optimization 422 | - Equality Constrained Optimization 423 | - Applications: Mechanical Design, Model-Predictive Control, Optimization in Finance 424 | - Automatic Differentiation and Neural Networks 425 | - Vector Spaces, Norms and the Projection Theorem 426 | - Fast First-Order Optimization 427 | - Duality and Primal-Dual Algorithms 428 | - SVM and the Reproducing Kernel Hilbert Space 429 | - Conic Programming 430 | - Alternating Methods and the EM Algorithm 431 | - Applications: Graph Problems, Computer Vision and Generalized Low-Rank Models 432 | - Gradient-Free and Non-Convex Optimization 433 | 434 | πŸŽ₯ Lectures on YouTube : https://youtube.com/playlist?list=PLdkTDauaUnQpzuOCZyUUZc0lxf4-PXNR5&si=x3fYVDBXH7Y4TAmY 435 | 436 | 437 | ### Probabilistic Reasoning & Machine Learning 438 | TU Dortmund, Prof. Stefan Harmeling, 2022 439 | 440 | ![image](https://github.com/user-attachments/assets/95b5be7d-6d5f-4c0c-9e3a-b4198d3270ac) 441 | 442 | πŸŽ₯ Video lectures (28 sessions): https://youtube.com/playlist?list=PLzrCXlf6ypbzDYKDchKfM-I9s20mFCL0q&si=IuKihyN1QdWIuY8d 443 | 444 | 445 | ### Parallel Computing and Scientific Machine Learning 446 | MIT, Dr. Chris Rackauckas, 2021 447 | 448 | ![image](https://github.com/user-attachments/assets/ece90288-73d3-4a0f-ad5c-af4163bfe572) 449 | 450 | 451 | - Getting Started with Julia 452 | - Optimizing Serial Code 453 | - Physics-Informed Neural Networks 454 | - Introduction to Discrete Dynamical Systems 455 | - The Basics of Single Node Parallel Computing 456 | - Styles of Parallelism 457 | - Ordinary Differential Equations 458 | - Forward-Mode Automatic Differentiation 459 | - Solving Stiff Ordinary Differential Equations 460 | - Basic Parameter Estimation, Reverse-Mode AD, and Inverse Problems 461 | - Differentiable Programming and Neural Differential Equations 462 | - MPI for Distributed Computing 463 | - Mathematics of ML and HPC 464 | - GPU Computing 465 | - Partial Differential Equations and Convolutional Neural Networks 466 | - Probabilistic Programming 467 | - Global Sensitivity Analysis 468 | - Code Profiling and Optimization 469 | - Uncertainty Programming and Generalized Uncertainty Quantification 470 | 471 | πŸŽ₯ Video Lectures: https://youtube.com/playlist?list=PLCAl7tjCwWyGjdzOOnlbGnVNZk0kB8VSa&si=-5MJhyhshyQ1SpcQ 472 | πŸ“” Lecture notes as an online book: https://book.sciml.ai/ 473 | 474 | 475 | 476 | 477 | ### Machine Learning and Bayesian Inference 478 | University of Cambridge, Dr. Sean Holden 479 | 480 | ![image](https://github.com/user-attachments/assets/4e3d49f0-32ed-43cd-b48c-669962c3dd46) 481 | 482 | 483 | YouTube Playlist(15 lectures): https://youtube.com/playlist?list=PLdLk2RYEiAhp9Slj6F_LCMXUv7_Fi3V_Y&si=E-A3Igj-C3xrQJU2 484 | 485 | ### Spectral Graph Theory 486 | Iowa State University (2017), Prof. Steve Butler 487 | 488 | ![image](https://github.com/user-attachments/assets/39830572-2377-40c9-9383-cf1d7f89146a) 489 | 490 | 491 | πŸŽ₯ Lectures (32 Sessions): https://www.youtube.com/playlist?list=PLi4h0n4UP8d9VGPqO8vLQga9ZApO65TLW 492 | πŸ“” Textbook: An Introduction to the Theory of Graph Spectra 493 | 494 | 495 | ### Algorithmic Game Theory 496 | Stanford, Prof. Tim Roughgarden 497 | 498 | ![image](https://github.com/user-attachments/assets/52533a7d-aff5-4b9b-9f46-2dc23060155a) 499 | 500 | - Mechanism Design Basics 501 | - Myerson's Lemma 502 | - Algorithmic Mechanism Design 503 | - Revenue-Maximizing Auctions 504 | - Simple Near-Optimal Auctions 505 | - VCG Mechanism 506 | - Spectrum Auctions 507 | - Beyond Quasi-Linearity 508 | - Kidney Exchange, Stable Matching 509 | - Selfish Routing and the POA 510 | - Network Over-Provisioning 511 | - Hierarchy of Equilibrium Concepts 512 | - Smooth Games 513 | - Best-Case and Strong Nash Equilibria 514 | - Best-Response Dynamics 515 | - No-Regret Dynamics 516 | - Swap Regret; Minimax 517 | - Pure NE and PLS-Completeness 518 | - Mixed NE and PPAD-Completeness 519 | 520 | πŸŽ₯ Lectures: https://youtube.com/playlist?list=PLEGCF-WLh2RJBqmxvZ0_ie-mleCFhi2N4&si=7r52R_RF8miNr_N2 521 | 522 | 523 | ### Advanced Mechanism Design 524 | Stanford, Prof. Tim Roughgarden 525 | 526 | ![image](https://github.com/user-attachments/assets/ce009b28-9401-4358-b553-92e9126aa2cd) 527 | 528 | - Ascending and Ex Post Incentive Compatible Mechanisms 529 | - Unit-Demand Bidders and Walrasian Equilibria 530 | - The Crawford-Knoer Auction 531 | - The Clinching Auction 532 | - The Gross Substitutes Condition 533 | - Gross Substitutes-Welfare Maximization in Polynomial Time 534 | - Submodular Valuations 535 | - MIR and MIDR Mechanisms 536 | - MIDR Mechanisms via Scaling Algorithms 537 | - Coverage Valuations and Convex Rounding 538 | - Undominated Implementations and the Shrinking Auction 539 | - Bayesian Incentive-Compatibility 540 | - Black Box Reductions 541 | - The Price of Anarchy in Simple Auctions 542 | - The Price of Anarchy of Bayes-Nash Equilibria 543 | - The Price of Anarchy in First-Price Auctions 544 | - Demand Reduction in Multi-Unit Auctions Revisited 545 | - Beyond Smoothness and XOS Valuations 546 | - Multi-Parameter Revenue-Maximization 547 | - Interim Rules and Border’s Theorem 548 | - Characterization of Revenue-Maximizing Auctions 549 | 550 | πŸŽ₯ Lectures: https://youtube.com/playlist?list=PLEGCF-WLh2RI77PL4gwLld_OU9Zh3TCX9 551 | 552 | 553 | ### Algorithms and Uncertainty 554 | Prof. Thomas Kesselheim 555 | 556 | ![image](https://github.com/user-attachments/assets/50f1c37e-c4bd-4820-b36b-42224a81ccea) 557 | 558 | 559 | - Online Algorithms 560 | - Online Learning Algorithms and Online Convex Optimization 561 | - Markov Decision Processes 562 | - Stochastic and Robust Optimization 563 | 564 | πŸŽ₯ Lectures: https://www.youtube.com/playlist?list=PLyzcvvgje7aDZRFMJZgaVgOW5t5KLvD1- 565 | 566 | 567 | ### Information Geometry & its Applications 568 | University of California, Prof. Melvin Leok, San Diego, 2022 569 | 570 | ![image](https://github.com/user-attachments/assets/cb8e22e4-8fdc-4461-aa64-36131aaf7aed) 571 | 572 | 573 | πŸŽ₯ Lectures: https://www.youtube.com/playlist?list=PLHZhjPByiV3L94AeJ9FcK1yrnRDOt3Vit 574 | 575 | 576 | ### Advanced Scientific Computing 577 | The University of Iceland, Prof. Ing Morris Riedel 578 | 579 | 580 | ![image](https://github.com/user-attachments/assets/1cb8a825-0beb-40a7-8777-491a7a7fe34e) 581 | 582 | High Performance Computing 583 | - Parallel Programming with MPI 584 | - Parallelization Fundamentals 585 | - Advanced MPI Techniques 586 | - Parallel Algorithms & Data Structures 587 | - Parallel Programming with OpenMP 588 | - Hybrid Programming & Patterns 589 | - Debugging & Profiling & Performance Analysis 590 | - Accelerators & Graphical Processing Units 591 | - Parallel & Scalable Machine & Deep Learning 592 | - HPC in Health & Neurosciences 593 | - Computational Fluid Dynamics & Finite Elements 594 | - Systems Biology & Bioinformatics 595 | - Molecular Systems & Material Sciences 596 | - Terrestrial Systems & Climate 597 | 598 | 599 | πŸŽ₯ 2024 Lectures (ongoing): https://www.youtube.com/playlist?list=PLmJwSK7qduwVAnNfpueCgQqfchcSIEMg9 600 | πŸŽ₯ 2023 Lectures: https://www.youtube.com/playlist?list=PLmJwSK7qduwUBwrFn3SY8vi4AYa2rVTWH 601 | πŸŽ₯ 2022 Lectures: https://www.youtube.com/playlist?list=PLmJwSK7qduwWyqcSEB45HOyxq--z8njix 602 | 603 | 604 | ### Deep Learning in Scientific Computing 605 | ETH ZΓΌrich, Prof. Siddhartha Mishra, Dr. Benjamin Moseley, 2023 606 | 607 | ![image](https://github.com/user-attachments/assets/c468b2e5-684a-4285-a920-0762b2c18b9a) 608 | 609 | - Introduction to Deep Learning 610 | - Physics-Informed Neural Networks 611 | - Operator Learning 612 | - Neural Operators 613 | - Fourier Neural Operators and Convolutional Neural Operators 614 | - Differentiable Physics 615 | 616 | πŸŽ₯ Course lectures: https://www.youtube.com/playlist?list=PLJkYEExhe7rYY5HjpIJbgo-tDZ3bIAqAm 617 | 618 | 619 | ### Topology and Geometry 620 | Prof. Tadashi Tokieda 621 | 622 | ![image](https://github.com/user-attachments/assets/f33e4d2f-9aba-48fe-bec2-9e0fa8986a81) 623 | 624 | πŸŽ₯ Lectures: https://www.youtube.com/playlist?list=PLTBqohhFNBE_09L0i-lf3fYXF5woAbrzJ 625 | 626 | 627 | ### Deep Reinforcement Learning 628 | UC Berkeley, Prof. Sergey Levine 629 | 630 | ![image](https://github.com/user-attachments/assets/2721517d-9f3e-4f53-89b9-875f411c2ffd) 631 | 632 | 633 | In addition to the standard RL topics, the course also includes: 634 | - RL and language models 635 | - Offline RL 636 | - Inverse RL 637 | - RL as probabilistic inference 638 | - Uncertainty and RL 639 | - Transfer learning and meta learning 640 | 641 | πŸŽ₯ Lectures(2021-2023): https://www.youtube.com/playlist?list=PL_iWQOsE6TfVYGEGiAOMaOzzv41Jfm_Ps 642 | 643 | 644 | ### Information Theory 645 | Harvard, Prof. Gregory Falkovich, 2022 646 | 647 | ![image](https://github.com/user-attachments/assets/4b999c80-13b6-4f30-8a66-81b87438f61c) 648 | 649 | πŸŽ₯ Lectures: https://www.youtube.com/playlist?list=PLDEN2FPNHwVZKAFqfFl1b_NNAESTJwV9o 650 | πŸ“” Textbook (Physical Nature of Information): https://www.weizmann.ac.il/complex/falkovich/sites/complex.falkovich/files/uploads/PNI22.pdf 651 | 652 | 653 | ### Bayesian Statistics 654 | Virginia Tech, Prof. Scotland Leman, 2023 655 | 656 | ![image](https://github.com/user-attachments/assets/41294c78-8162-4390-a9f1-9619e006a08b) 657 | 658 | 659 | - Philosophy: What is probability? 660 | - Fisher vs Neyman vs Jeffreys. 661 | - The Likelihood Principle 662 | - Basic Bayesian constructions: Likelihoods, priors and posteriors 663 | - Exponential families and conjugate priors 664 | - Asymptotics, Bayesian t-tests, mixture models, hierarchical modeling, etc.. 665 | - Bayesian sequential updating 666 | - More on priors: Jeffreys, Reference, Objective, Subjective, etc... 667 | - Simulation procedures: Gibbs, Metropolis, etc... 668 | - Model Selection: Theory and Computational Approaches 669 | 670 | 671 | πŸŽ₯ Video lectures for the 2023 course and also lectures for the past semesters: https://www.youtube.com/@lemanlectures8611/videos 672 | πŸŽ₯ First lecture: https://youtu.be/vHAoj0Q5Auw?si=68ymPihUCaAmvvgK 673 | 674 | 675 | ### Random Matrices and Machine Learning 676 | Saarland University, Prof. Roland Speicher, 2023 677 | 678 | ![image](https://github.com/user-attachments/assets/d60af264-e703-4f35-9f5b-3f686b68b9ba) 679 | 680 | πŸŽ₯ Recorded videos (29 lectures): https://youtube.com/playlist?list=PLY11JnnnTUCabY4nc0hKptrd5qEWtLoo2&si=9HLbybgfW6pBss88 681 | 682 | 683 | ### Computational Topology 684 | University of Utah, Prof. Bei Wang, 2021 685 | 686 | ![image](https://github.com/user-attachments/assets/cf440b04-cb01-4691-a0d2-19c7eb4b09e9) 687 | 688 | 689 | - Basic concepts (graphs, connected components, topological space, manifold, point cloud samples) 690 | - Combinatorial structures on point cloud data (simplicial complexes) 691 | - New techniques in dimension reduction (circular coordinates, etc.) 692 | - Clustering (topology-based data partition, classification) 693 | - Homology and persistent homology 694 | - Topological signatures for classification 695 | - Structural inference and reconstruction from data 696 | - Topological algorithms for massive data 697 | - Deep learning with TDA 698 | - Multivariate and high-dimensional data analysis 699 | - Topological data analysis for visualization (vector fields, topological structures) 700 | - Practical applications of TDA 701 | 702 | πŸŽ₯ Playlist on YouTube (28 Lectures) : https://youtube.com/playlist?list=PLDZ6LA16SDbIvbgmCjcCuTA7mttfXjiec&si=FiadJKIdmKlJUIY7 703 | 704 | 705 | ### Optimal Transport 706 | Prof. Brittany Hamfeldt 707 | 708 | ![image](https://github.com/user-attachments/assets/13feaab0-c34c-420f-ac97-27312dcca17b) 709 | 710 | πŸŽ₯ Video Lectures: https://youtube.com/playlist?list=PLJ6garKOlK2qKVhRm6UwvcQ46wK-ciHbl&si=zeG5RCK_E04SRNww 711 | 712 | 713 | ### Group Theory 714 | Prof. Richard Borcherds 715 | 716 | ![image](https://github.com/user-attachments/assets/4566a212-d4e8-49e6-9ffe-765f60beb12d) 717 | 718 | πŸŽ₯ Lectures: https://www.youtube.com/@richarde.borcherds7998/playlists 719 | 720 | 721 | ### Manifold Learning, Optimization and Information Geometry 722 | Politecnico di Milano 2022 723 | 724 | ![image](https://github.com/user-attachments/assets/59fde121-bf22-4d37-93a5-8bcef4a4af80) 725 | 726 | πŸŽ₯ Lectures: https://youtube.com/playlist?list=PLvVaDdaHGtpesn2DHUo6ete-1pPhT1xzj&si=24WgTbFLChWMaJRx 727 | 728 | 729 | ### Random Matrix Theory 730 | King's College London, Dr Pierpaolo Vivo 731 | 732 | ![image](https://github.com/user-attachments/assets/61e34a9c-3d6a-4c10-9ef4-bb9941e6a7bf) 733 | 734 | πŸŽ₯ Lectures : https://www.youtube.com/playlist?list=PLyHAvCibkccQEFYXdM6r8WG4GQULRKmRA 735 | 736 | 737 | ### Topological Data Analysis 738 | Colorado State University, Henry Adams, 2021 739 | 740 | ![image](https://github.com/user-attachments/assets/2da24886-19c9-4db3-9540-ede0b4b7b2e3) 741 | 742 | πŸŽ₯ Videos (27 short lectures) : https://www.math.colostate.edu/~adams/teaching/dsci475spr2021/ 743 | 744 | 745 | ### Matrix Calculus for Machine Learning and Beyond 746 | MIT, Prof. Alan Edelman, Prof. Steven G. Johnson, 2023 747 | 748 | ![image](https://github.com/user-attachments/assets/7f6bff74-51ef-4caa-8f6c-62cd2d02abfe) 749 | 750 | πŸŽ₯ YouTube (8 lectures): https://youtube.com/playlist?list=PLUl4u3cNGP62EaLLH92E_VCN4izBKK6OE&si=rNoLocGXOEXBQjMH 751 | 752 | 753 | ### Probabilistic Machine Learning 754 | University of TΓΌbingen, Dr. Philipp Hennig, 2023 755 | 756 | ![image](https://github.com/user-attachments/assets/ec7dbeee-c289-4c70-962e-40e7922aa206) 757 | 758 | 759 | - Reasoning Under Uncertainty 760 | - Continuous Random Variables 761 | - Exponential Families 762 | - Gaussian Probability Distributions 763 | - Parametric Regression 764 | - Gaussian Processes 765 | - Understanding Gaussian Processes 766 | - GP Regression 767 | - Understanding Kernels and Gaussian Processes 768 | - The role of Linear Algebra in Gaussian Processes 769 | - Computation and Inference 770 | - Logistic Regression 771 | - GP Classification 772 | - Deep Learning 773 | - Probabilistic Deep Learning 774 | - Uncertainty in Deep Learning 775 | - Uses of Uncertainty for Deep Learning 776 | - Gauss-Markov Models 777 | - Parameter Inference 778 | - Variational Inference 779 | 780 | 781 | πŸŽ₯ Lectures (25 lectures): https://youtube.com/playlist?list=PL05umP7R6ij2YE8rRJSb-olDNbntAQ_Bx&si=qivnfDBYjFOu1TOk 782 | πŸ“” Slides: https://github.com/philipphennig/Probabilistic_ML 783 | 784 | 785 | ### Discrete Differential Geometry 786 | Carnegie Mellon Universit 787 | 788 | ![image](https://github.com/user-attachments/assets/baf9fc2a-14a8-4627-b42c-651fc81d72f9) 789 | 790 | πŸŽ₯ Lectures : https://www.youtube.com/playlist?list=PL9_jI1bdZmz0hIrNCMQW1YmZysAiIYSSS 791 | 792 | ### Applied Numerical Algorithms 793 | MIT, Prof. Justin Solomon, 2023 794 | 795 | ![image](https://github.com/user-attachments/assets/9dbd7f68-2c58-447b-a93f-f6ee483b7118) 796 | 797 | πŸŽ₯ Lectures : https://www.youtube.com/watch?v=Xt4p5gk24ss 798 | 799 | ### Shape Analysis 800 | MIT, Prof. Justin Solomon 801 | 802 | ![image](https://github.com/user-attachments/assets/3f5e7038-d0ee-48d9-b5ab-e41391c97f41) 803 | 804 | 805 | πŸŽ₯ Lectures : https://www.youtube.com/watch?v=VjyBp6PrvB4 806 | 807 | --------------------------------------------------------------------------------