{"id":28716169,"url":"https://github.com/amirabbasasadi/mathematics-computerscience-courses","last_synced_at":"2025-10-05T19:55:32.723Z","repository":{"id":259397504,"uuid":"877702145","full_name":"amirabbasasadi/mathematics-computerscience-courses","owner":"amirabbasasadi","description":"A collection of awesome mathematics and computer science courses 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Mathematics / Computer Science Courses \nA collection of some useful mathematics and computer science courses \n\n\n## Courses\n\n### Deep Learning for Computer Vision\nProf. Justin Johnson, University of Michigan, 2019\n\n![image](https://github.com/user-attachments/assets/371501d1-21d4-4b2a-bba9-c8e51d2dd62a)\n\n- Linear classifiers\n- Stochastic gradient descent\n- Fully-connected networks\n- Convolutional networks\n- Recurrent networks\n- Attention and transformers\n- Object detection\n- Image segmentation\n- Video classification\n- Generative models (GANs, VAEs, autoregressive models)\n- Reinforcement Learning\n\n🎥 Lectures: https://youtube.com/playlist?list=PL5-TkQAfAZFbzxjBHtzdVCWE0Zbhomg7r \n\n\n### High-Dimensional Probability\nRoman Vershynin\n\n![image](https://github.com/user-attachments/assets/37f50b44-67e2-4136-abb8-a74857ca56ae)\n\n🎥 Lectures: https://youtube.com/playlist?list=PLPjEEUWIWhQV7X6dXfrVP3w0KBBLBVJ0j  \n📔 Textbook : https://www.math.uci.edu/~rvershyn/papers/HDP-book/HDP-book.html\n\n\n### Deep Unsupervised Learning\nProf. Pieter Abbeel, UC Berkeley, 2024\n\n![image](https://github.com/user-attachments/assets/eb151b94-f8fe-4bb5-8d0d-28525eee6532)\n\n- Autoregressive Models\n- Flow Models\n- Latent Variable Models \u0026 Variational AutoEncoders (VAEs)\n- Generative Adversarial Networks (GANs)\n- Diffusion Models\n- Self-Supervised Learning\n- Large Language Models (LLMs)\n- Generative Video\n- Semisupervised Learning \u0026 Unsupervised Distribution Alignment\n- Generative Modeling for Science\n- Neural Radiance Fields\n- Multimodal Models\n- Parallelization\n\n🎥 Lectures: https://youtube.com/playlist?list=PLwRJQ4m4UJjPIvv4kgBkvu_uygrV3ut_U\n\n\n### Deep Generative Models\nProf. Stefano Ermon, Stanford University, 2023\n\n![image](https://github.com/user-attachments/assets/ead0b70a-a22f-4c6b-b008-45c961b0b078)\n\n- Autoregressive Models\n- Maximum Likelihood Learning\n- Variational AutoEncoders (VAEs)\n- Normalizing Flows\n- Generative Adversarial Networks (GANs)\n- Energy Based Models (EBMs)\n- Score Based Models\n- Evaluation of Generative Models\n\n🎥 Lectures: https://youtube.com/playlist?list=PLoROMvodv4rPOWA-omMM6STXaWW4FvJT8      \n📔 Course page containing lecture notes: https://deepgenerativemodels.github.io\n\n\n### Geometric Deep Learning\nAfrican Master’s in Machine Intelligence, 2022\n\n![image](https://github.com/user-attachments/assets/105b669d-d05d-4742-b1c6-1b6ecf7c274e)\n\n- High-Dimensional Learning\n- Geometric Priors\n- Graphs \u0026 Sets\n- Grids\n- Groups\n- Geodesics \u0026 Manifolds\n- Gauges\n  \n🎥 Lectures: https://youtube.com/playlist?list=PLn2-dEmQeTfSLXW8yXP4q_Ii58wFdxb3C                 \n📔 webpage : https://geometricdeeplearning.com\n\n\n### Machine Learning with Graphs\nProf. Jure Leskovec, Stanford University, 2021\n\n![image](https://github.com/user-attachments/assets/bc56580c-b0a2-459f-846a-0d6f40ac2f74)\n\n🎥 Lectures: https://youtube.com/playlist?list=PLoROMvodv4rPLKxIpqhjhPgdQy7imNkDn\n\n\n### Reinforcement Learning Theory \nProf. Csaba Szepesvári, University of Alberta, 2022 \n\n![image](https://github.com/user-attachments/assets/ec632aea-95ad-4077-ae44-796af53b9326)  \n\n- MDP, Fundamental Theorem\n- Value and Policy Iteration\n- Local Planning\n- Function Approximation\n- Approximate Policy Iteration\n- Planning Complexity, TensorPlan\n- Lower Bound for API and POLITEX\n- Policy Search\n- Batch RL\n- Online RL\n\n🎥 Lectures: [https://www.youtube.com/playlist?list=PLQCZ7_TRKVIzODPXorEyvhCk25TlcTANC\n](https://www.youtube.com/playlist?list=PLQCZ7_TRKVIzODPXorEyvhCk25TlcTANC)   \n📔 webpage : https://rltheory.github.io/\n\n\n### Multi-Agent Reinforcement Learning\nDr. Stefano V. Albrecht, 2023  \n![image](https://github.com/user-attachments/assets/465dd8a7-25c2-4f5b-b95d-a6a0f934587b)  \n\n🎥 Lectures: [https://www.youtube.com/playlist?list=PLkoCa1tf0XjCU6GkAfRCkChOOSH6-JC_2](https://www.youtube.com/playlist?list=PLkoCa1tf0XjCU6GkAfRCkChOOSH6-JC_2)  \n📔 Textbook : http://www.marl-book.com/ \n\n\n### Numerics of Machine Learning\nUniversity of Tübingen, 2023  \n\n![image](https://github.com/user-attachments/assets/8dce5eb8-5d27-47b0-9cbb-e5def426f103)  \n\n\n- Numerical Linear Algebra\n- Scaling Gaussian Processes\n- Computation-aware Gaussian Processes\n- State Space Models\n- Solving Ordinary Differential Equations\n- Probabilistic Numerical ODE Solvers\n- Partial Differential Equations\n- Monte Carlo\n- Bayesian Quadrature\n- Optimization for Deep Learning\n- Second-order Optimization for Deep Learning\n- Uncertainty in Deep Learning\n\n🎥 Lectures : https://www.youtube.com/playlist?list=PL05umP7R6ij2lwDdj7IkuHoP9vHlEcH0s   \n\n\n### Reinforcement Learning\nStanford, Prof. Emma Brunskill, 2024  \n\n![image](https://github.com/user-attachments/assets/96785d68-6730-49d0-9d4a-b91a2009ebac)  \n\n- Introduction to Reinforcement Learning\n- Tabular MDP planning\n- Policy Evaluation\n- Q learning and Function Approximation\n- Policy Search\n- Offline RL\n- Exploration\n- Multi-Agent Games\n- Value Alignment\n\n \n🎥 Lectures: https://www.youtube.com/playlist?list=PLoROMvodv4rN4wG6Nk6sNpTEbuOSosZdX  \n\n\n### Model-Based Image and Signal Processing\nPurdue University, Prof. Charles A. Bouman, 2020  \n\n![image](https://github.com/user-attachments/assets/2a506822-0dba-4f8d-9c2f-994913b6111f)  \n\n- Probability\n- Causal Gaussian Models\n- Non-Causal Gaussian Models\n- MAP with Gaussian Priors\n- Non-Gaussian Markov Random Fields\n- Non-Gaussian MAP\n- Majorization\n- Constrained Optimization\n- Plug and Play\n- EM Algorithm\n- Markov Chains and HMMs\n- General MRFs\n- Stochastic Simulation\n- MAP Segmentation\n\n🎥 Lectures : https://www.youtube.com/playlist?list=PL3ZrjaBngMS0mTSoMsy7P6rTFSgsmsMw3  \n📔 Textbook: https://engineering.purdue.edu/~bouman/publications/FCI-book/  \n\n\n\n### Convex Optimization\nStanford, Prof. Stephen Boyd, 2023  \n\n![image](https://github.com/user-attachments/assets/a30ba279-6fd2-4045-893b-b1535c1e0bdb)  \n\n- Convex sets\n- Convex functions\n- Convex optimization problems\n- Duality\n- Approximation and fitting\n- Statistical estimation\n- Geometric problems\n- Numerical linear algebra background\n- Unconstrained minimization\n- Equality constrained minimization\n- Interior-point methods\n\n🎥 Lectures: https://www.youtube.com/playlist?list=PLoROMvodv4rMJqxxviPa4AmDClvcbHi6h  \n📔 Textbook : https://stanford.edu/~boyd/cvxbook/  \n\n\n### Learning in Games and Games in Learning\nUniversity of Pennsylvania, Prof. Aaron Roth, 2023  \n\n![image](https://github.com/user-attachments/assets/a1d55878-2c1a-4ccc-9e89-744c92fd7521)   \nA mathematical course focusing on the interplay between game theory and machine learning:\n- Introduction to sequential learning\n- Halving algorithm\n- Follow the perturbed leader\n- Follow the regularized leader\n- Online convex optimization\n- Zero-sum games, Minimax theorem\n- Deriving a no regret learning algorithm\n- Correlated equilibrium, Swap regret\n- The adversary moves first framework\n- Multi-objective sequential learning \n\n🎥 YouTube (24 lectures) : https://www.youtube.com/playlist?list=PLlIlhe_rS4U2p_fxYwB15nhzHEKF53xUl  \n📔 Lecture notes: https://mlgametheory.com/\n\n\n### Foundations of Reinforcement Learning\nPrinceton University, Prof. Chi Jin, 2024  \n\n![image](https://github.com/user-attachments/assets/7e879ebd-e311-48b7-bb76-7b6c42da3ae9)  \nA graduate level course on theoretical foundations of reinforcement learning:  \n- MDP basics and planning\n- Concentration inequalities, Martingale concentrations\n- Generative models, value iteration\n- Online RL, exploration, optimism\n- Minimax lower bound\n- Offline RL, pessimism\n- Policy optimization\n- Large state space, linear function approximation\n- General function approximation\n- Game theory and multiagent RL\n- Learning Markov games\n- Partial observable MDP\n\n\n🎥 YouTube (22 lectures) : https://www.youtube.com/playlist?list=PLYXvCE1En13epbogBmgafC_Yyyk9oQogl  \n📔 Course page containing lecture notes: https://sites.google.com/view/cjin/teaching/ece524\n\n\n\n### Graduate Topics in Deep Learning Theory\nHarvard Center of Mathematical Sciences and Applications, Dr. Eli Grigsby, 2024  \n\n![image](https://github.com/user-attachments/assets/e8dba6f0-7dab-443f-9fa6-033b42e8ed8c)  \n\nA course on geometric aspects of deep learning theory:\n- The geometry and combinatorics of feedforward ReLU neural networks as piecewise linear function classes\n- Neural networks as universal approximators: discrete and non-discrete versions\n- The role of the superposition hypothesis in mechanistic interpretability of neural networks \n- Neural network architectures for sequence-to-sequence processing\n- Representing finite state automata using sequence-to-sequence architectures\n- Geometric distortion in deep networks and the importance of residual connections\n- Symmetries of overparameterized ReLU neural networks, optimization, and generalization\n- Algorithmic computation of topological invariants of decision boundaries/regions\n\n🎥 YouTube: https://youtube.com/playlist?list=PL0NRmB0fnLJSEXFQHGF0q5JcedxTqK4AJ\u0026si=G0rk4GBgywt6kypK  \n📔 Course page : https://sites.google.com/bc.edu/eli-grigsby/mt875-mechanistic-interpretability\n\n\n### Probabilistic Programming \nUniversity of British Columbia, Dr. Frank Wood, 2021  \n\n![image](https://github.com/user-attachments/assets/8293bbb3-42a2-4a9b-9288-fe5941422aa3)  \n\n\n- Introduction to Model-Based Reasoning\n- Graphical Models\n- Inference, Learning, Monte Carlo, Sampling\n- Markov Chain Monte Carlo\n- First Order Probabilistic Programming Languages\n- Graphical Model Compilation\n- Graph-Based Inference\n- Hamiltonian Monte Carlo\n- Evaluation-based Inference\n- Variational Inference\n- Higher Order Probabilistic Programming Languages\n- Amortized Inference / Guide Programs / Inference Compilation\n- Reinforcement Learning as Inference\n- Alternative Variational Bounds\n- Reparametrization and Normalizing Flows\n\n🎥 25 lectures on YouTube: https://youtube.com/playlist?list=PLRBUAK6di_6XlF7KAZBPRgcP0zD5sVXcN\u0026si=9hjsRE1bav7vTqbG  \n📔 An Introduction to Probabilistic Programming: https://arxiv.org/abs/1809.10756\n\n\n### Learning and Reasoning with Bayesian Networks\nUCLA, Prof. Adnan Darwiche  \n\n![image](https://github.com/user-attachments/assets/cfac9ac2-0711-4a78-b87c-7f0e0d258cd1)  \n\n\n- Propositional Logic\n- Probability Calculus: Beliefs and Hard Evidence, Soft Evidence\n- Bayesian Networks: Syntax and Semantics\n- Bayesian Networks: Independence and d-Separation\n- Probabilistic Queries and their Complexity\n- Building Bayesian Networks\n- Inference by Variable Elimination\n- The Jointree Algorithm\n- Inference by Conditioning\n- Arithmetic Circuits\n- Loopy Belief Propagation\n- Learning Parameters\n- Learning Network Structure\n- Bayesian Learning\n- Causality\n- Sensitivity Analysis\n- Reasoning about Classifiers\n- Explaining Classifiers\n\n\n🎥 YouTube Playlist(32 lectures + 4 additional lectures on causality): https://youtube.com/playlist?list=PLlDG_zCuBub6ywAIrM1DfJp8xaeVjyvwx  \n📔 Textbook: Modeling and Reasoning with Bayesian Networks, Adnan Darwiche\n\n\n###  Kernel methods in machine learning\nENS Paris-Saclay, Dr. Julien Mairal, Dr. Jean-Philippe Vert  \n\n![image](https://github.com/user-attachments/assets/74964615-14cf-4d76-9771-563c24383417)\n\n\n- Positive definite kernels\n- Reproducing Kernel Hilbert Space\n- Smoothness functional, Kernel trick, Representer theorem\n- Kernel ridge and logistic regression\n- Large-margin classifiers, SVMs\n- Unsupervised kernel methods\n- Green, Mercer, Herglotz, Bochner and friends\n- Kernels for graphs\n- Multiple kernels learning\n- Large-scale learning\n- Deep kernel machines\n- Kernels for probabilistic models \n- Kernel mean embedding\n- Characteristic kernels\n\n\n🎥 YouTube Playlist (25 lectures): https://www.youtube.com/playlist?list=PLD93kGj6_EdrkNj27AZMecbRlQ1SMkp_o  \n\n\n### Advanced Robotics\n\nUC Berkeley, Prof. Pieter Abbeel, 2019  \n\n![image](https://github.com/user-attachments/assets/dbc60104-f42a-47d9-9dd9-cac6a2b61316)  \n\n- Markov Decision Processes: Exact Methods\n- Discretization of Continuous State Space MDPs\n- Function Approximation\n- LQR, iterative LQR, Differential Dynamic Programming\n- Unconstrained Optimization\n- Constrained Optimization\n- Optimization-based Control\n- Motion Planning\n- Kalman Filtering, EKF, UKF\n- Smoother, MAP, Maximum Likelihood, EM, KF parameter estimation\n- Particle Filters\n- Partially Observable MDPs\n- Imitation Learning\n- RL : Policy Gradients, Off-policy RL, Model-based RL\n- Physics Simulation\n\n🎥 YouTube Playlist (24 lectures) : https://youtube.com/playlist?list=PLwRJQ4m4UJjNBPJdt8WamRAt4XKc639wF\u0026si=LrZXaiXafs6Qj07x  \n\n\n\n###  Statistical Machine Learning\nCarnegie Mellon University, Prof. Larry Wasserman, 2016\n\n![image](https://github.com/user-attachments/assets/ba4ba579-a388-4a0f-b0d7-55f5946df30c)  \n\n\n- Function Spaces\n- Concentration of Measure\n- Linear Regression\n- Non-Parametric Regression\n- Trend Filtering\n- Linear Classification\n- Non-Parametric Classification\n- Minimax Theory\n- Non-Parametric Bayes\n- Boosting\n- Clustering\n- Graphical Models\n- Dimension Reduction\n- Random Matrix Theory\n- Differential Privacy\n\n🎥 YouTube Playlist (24 lectures) : https://youtube.com/playlist?list=PLTB9VQq8WiaCBK2XrtYn5t9uuPdsNm7YE\u0026si=T5N31V-7ZPA_onXN  \n\n\n\n\n### Optimization Methods for Machine Learning and Engineering\nKIT(2020), Dr. Julius Pfrommer  \n\n![image](https://github.com/user-attachments/assets/d644a17b-ffd6-49f5-a416-a970a68a27ff)  \n\n- Introduction, Convexity and Gradient Descent\n- Newton’s Method\n- Inequality Constrained Optimization\n- Equality Constrained Optimization\n- Applications: Mechanical Design, Model-Predictive Control, Optimization in Finance\n- Automatic Differentiation and Neural Networks\n- Vector Spaces, Norms and the Projection Theorem\n- Fast First-Order Optimization\n- Duality and Primal-Dual Algorithms\n- SVM and the Reproducing Kernel Hilbert Space\n- Conic Programming\n- Alternating Methods and the EM Algorithm\n- Applications: Graph Problems, Computer Vision and Generalized Low-Rank Models\n- Gradient-Free and Non-Convex Optimization\n\n🎥 Lectures on YouTube : https://youtube.com/playlist?list=PLdkTDauaUnQpzuOCZyUUZc0lxf4-PXNR5\u0026si=x3fYVDBXH7Y4TAmY  \n\n\n### Probabilistic Reasoning \u0026 Machine Learning\nTU Dortmund, Prof. Stefan Harmeling, 2022\n\n![image](https://github.com/user-attachments/assets/95b5be7d-6d5f-4c0c-9e3a-b4198d3270ac)  \n\n🎥 Video lectures (28 sessions): https://youtube.com/playlist?list=PLzrCXlf6ypbzDYKDchKfM-I9s20mFCL0q\u0026si=IuKihyN1QdWIuY8d\n\n\n### Parallel Computing and Scientific Machine Learning\nMIT, Dr. Chris Rackauckas, 2021  \n\n![image](https://github.com/user-attachments/assets/ece90288-73d3-4a0f-ad5c-af4163bfe572)  \n\n\n- Getting Started with Julia\n- Optimizing Serial Code\n- Physics-Informed Neural Networks\n- Introduction to Discrete Dynamical Systems\n- The Basics of Single Node Parallel Computing\n- Styles of Parallelism\n- Ordinary Differential Equations\n- Forward-Mode Automatic Differentiation\n- Solving Stiff Ordinary Differential Equations\n- Basic Parameter Estimation, Reverse-Mode AD, and Inverse Problems\n- Differentiable Programming and Neural Differential Equations\n- MPI for Distributed Computing\n- Mathematics of ML and HPC\n- GPU Computing\n- Partial Differential Equations and Convolutional Neural Networks\n- Probabilistic Programming\n- Global Sensitivity Analysis\n- Code Profiling and Optimization\n- Uncertainty Programming and Generalized Uncertainty Quantification\n \n🎥 Video Lectures: https://youtube.com/playlist?list=PLCAl7tjCwWyGjdzOOnlbGnVNZk0kB8VSa\u0026si=-5MJhyhshyQ1SpcQ  \n📔 Lecture notes as an online book: https://book.sciml.ai/\n\n\n\n\n### Machine Learning and Bayesian Inference\nUniversity of Cambridge, Dr. Sean Holden  \n\n![image](https://github.com/user-attachments/assets/4e3d49f0-32ed-43cd-b48c-669962c3dd46)\n\n\nYouTube Playlist(15 lectures): https://youtube.com/playlist?list=PLdLk2RYEiAhp9Slj6F_LCMXUv7_Fi3V_Y\u0026si=E-A3Igj-C3xrQJU2 \n\n### Spectral Graph Theory\nIowa State University (2017), Prof. Steve Butler  \n\n![image](https://github.com/user-attachments/assets/39830572-2377-40c9-9383-cf1d7f89146a)  \n\n\n🎥 Lectures (32 Sessions): https://www.youtube.com/playlist?list=PLi4h0n4UP8d9VGPqO8vLQga9ZApO65TLW  \n📔 Textbook: An Introduction to the Theory of Graph Spectra\n\n\n### Algorithmic Game Theory  \nStanford, Prof. Tim Roughgarden  \n\n![image](https://github.com/user-attachments/assets/52533a7d-aff5-4b9b-9f46-2dc23060155a)  \n\n- Mechanism Design Basics \n- Myerson's Lemma \n- Algorithmic Mechanism Design \n- Revenue-Maximizing Auctions \n- Simple Near-Optimal Auctions \n- VCG Mechanism \n- Spectrum Auctions \n- Beyond Quasi-Linearity \n- Kidney Exchange, Stable Matching \n- Selfish Routing and the POA \n- Network Over-Provisioning\n- Hierarchy of Equilibrium Concepts \n- Smooth Games \n- Best-Case and Strong Nash Equilibria \n- Best-Response Dynamics \n- No-Regret Dynamics \n- Swap Regret; Minimax \n- Pure NE and PLS-Completeness \n- Mixed NE and PPAD-Completeness\n\n🎥 Lectures: https://youtube.com/playlist?list=PLEGCF-WLh2RJBqmxvZ0_ie-mleCFhi2N4\u0026si=7r52R_RF8miNr_N2  \n\n\n### Advanced Mechanism Design\nStanford, Prof. Tim Roughgarden  \n\n![image](https://github.com/user-attachments/assets/ce009b28-9401-4358-b553-92e9126aa2cd)\n\n- Ascending and Ex Post Incentive Compatible Mechanisms\n- Unit-Demand Bidders and Walrasian Equilibria\n- The Crawford-Knoer Auction\n- The Clinching Auction\n- The Gross Substitutes Condition\n- Gross Substitutes-Welfare Maximization in Polynomial Time\n- Submodular Valuations\n- MIR and MIDR Mechanisms\n- MIDR Mechanisms via Scaling Algorithms\n- Coverage Valuations and Convex Rounding\n- Undominated Implementations and the Shrinking Auction\n- Bayesian Incentive-Compatibility\n- Black Box Reductions\n- The Price of Anarchy in Simple Auctions\n- The Price of Anarchy of Bayes-Nash Equilibria\n- The Price of Anarchy in First-Price Auctions\n- Demand Reduction in Multi-Unit Auctions Revisited\n- Beyond Smoothness and XOS Valuations\n- Multi-Parameter Revenue-Maximization\n- Interim Rules and Border’s Theorem\n- Characterization of Revenue-Maximizing Auctions\n\n🎥 Lectures: https://youtube.com/playlist?list=PLEGCF-WLh2RI77PL4gwLld_OU9Zh3TCX9\n\n\n### Algorithms and Uncertainty\nProf. Thomas Kesselheim  \n\n![image](https://github.com/user-attachments/assets/50f1c37e-c4bd-4820-b36b-42224a81ccea)  \n\n\n- Online Algorithms\n- Online Learning Algorithms and Online Convex Optimization\n- Markov Decision Processes\n- Stochastic and Robust Optimization\n\n🎥 Lectures: https://www.youtube.com/playlist?list=PLyzcvvgje7aDZRFMJZgaVgOW5t5KLvD1-\n\n\n### Information Geometry \u0026 its Applications\nUniversity of California, Prof. Melvin Leok, San Diego, 2022  \n\n![image](https://github.com/user-attachments/assets/cb8e22e4-8fdc-4461-aa64-36131aaf7aed)\n\n\n🎥 Lectures: https://www.youtube.com/playlist?list=PLHZhjPByiV3L94AeJ9FcK1yrnRDOt3Vit\n\n\n### Advanced Scientific Computing  \nThe University of Iceland, Prof. Ing Morris Riedel  \n\n\n![image](https://github.com/user-attachments/assets/1cb8a825-0beb-40a7-8777-491a7a7fe34e)  \n\nHigh Performance Computing\n- Parallel Programming with MPI \n- Parallelization Fundamentals \n- Advanced MPI Techniques \n- Parallel Algorithms \u0026 Data Structures \n- Parallel Programming with OpenMP \n- Hybrid Programming \u0026 Patterns \n- Debugging \u0026 Profiling \u0026 Performance Analysis \n- Accelerators \u0026 Graphical Processing Units \n- Parallel \u0026 Scalable Machine \u0026 Deep Learning \n- HPC in Health \u0026 Neurosciences \n- Computational Fluid Dynamics \u0026 Finite Elements \n- Systems Biology \u0026 Bioinformatics \n- Molecular Systems \u0026 Material Sciences \n- Terrestrial Systems \u0026 Climate\n\n\n🎥 2024 Lectures (ongoing): https://www.youtube.com/playlist?list=PLmJwSK7qduwVAnNfpueCgQqfchcSIEMg9   \n🎥 2023 Lectures: https://www.youtube.com/playlist?list=PLmJwSK7qduwUBwrFn3SY8vi4AYa2rVTWH  \n🎥 2022 Lectures: https://www.youtube.com/playlist?list=PLmJwSK7qduwWyqcSEB45HOyxq--z8njix\n\n\n### Deep Learning in Scientific Computing\nETH Zürich, Prof. Siddhartha Mishra, Dr. Benjamin Moseley, 2023  \n\n![image](https://github.com/user-attachments/assets/c468b2e5-684a-4285-a920-0762b2c18b9a)  \n\n- Introduction to Deep Learning\n- Physics-Informed Neural Networks\n- Operator Learning\n- Neural Operators\n- Fourier Neural Operators and Convolutional Neural Operators\n- Differentiable Physics\n\n🎥 Course lectures: https://www.youtube.com/playlist?list=PLJkYEExhe7rYY5HjpIJbgo-tDZ3bIAqAm\n\n\n### Topology and Geometry  \nProf. Tadashi Tokieda  \n\n![image](https://github.com/user-attachments/assets/f33e4d2f-9aba-48fe-bec2-9e0fa8986a81)  \n\n🎥 Lectures: https://www.youtube.com/playlist?list=PLTBqohhFNBE_09L0i-lf3fYXF5woAbrzJ\n\n\n### Deep Reinforcement Learning\n UC Berkeley,  Prof. Sergey Levine  \n\n![image](https://github.com/user-attachments/assets/2721517d-9f3e-4f53-89b9-875f411c2ffd)\n\n\n In addition to the standard RL topics, the course also includes:\n- RL and language models\n- Offline RL\n- Inverse RL\n- RL as probabilistic inference\n- Uncertainty and RL\n- Transfer learning and meta learning\n\n🎥 Lectures(2021-2023): https://www.youtube.com/playlist?list=PL_iWQOsE6TfVYGEGiAOMaOzzv41Jfm_Ps\n\n\n### Information Theory\nHarvard, Prof. Gregory Falkovich, 2022  \n\n![image](https://github.com/user-attachments/assets/4b999c80-13b6-4f30-8a66-81b87438f61c)  \n\n🎥 Lectures: https://www.youtube.com/playlist?list=PLDEN2FPNHwVZKAFqfFl1b_NNAESTJwV9o  \n📔 Textbook (Physical Nature of Information): https://www.weizmann.ac.il/complex/falkovich/sites/complex.falkovich/files/uploads/PNI22.pdf\n\n\n### Bayesian Statistics \nVirginia Tech,  Prof. Scotland Leman, 2023  \n\n![image](https://github.com/user-attachments/assets/41294c78-8162-4390-a9f1-9619e006a08b)  \n\n\n- Philosophy: What is probability?\n- Fisher vs Neyman vs Jeffreys.\n- The Likelihood Principle\n- Basic Bayesian constructions: Likelihoods, priors and posteriors\n- Exponential families and conjugate priors\n- Asymptotics, Bayesian t-tests, mixture models, hierarchical modeling, etc..\n- Bayesian sequential updating\n- More on priors: Jeffreys, Reference, Objective, Subjective, etc...\n- Simulation procedures: Gibbs, Metropolis, etc...\n- Model Selection: Theory and Computational Approaches\n\n\n🎥 Video lectures for the 2023 course and also lectures for the past semesters: https://www.youtube.com/@lemanlectures8611/videos  \n🎥 First lecture: https://youtu.be/vHAoj0Q5Auw?si=68ymPihUCaAmvvgK\n\n\n### Random Matrices and Machine Learning\nSaarland University, Prof. Roland Speicher, 2023  \n\n![image](https://github.com/user-attachments/assets/d60af264-e703-4f35-9f5b-3f686b68b9ba)  \n\n🎥 Recorded videos (29 lectures): https://youtube.com/playlist?list=PLY11JnnnTUCabY4nc0hKptrd5qEWtLoo2\u0026si=9HLbybgfW6pBss88\n\n\n### Computational Topology\nUniversity of Utah, Prof. Bei Wang, 2021  \n\n![image](https://github.com/user-attachments/assets/cf440b04-cb01-4691-a0d2-19c7eb4b09e9)  \n\n\n- Basic concepts (graphs, connected components, topological space, manifold, point cloud samples)\n- Combinatorial structures on point cloud data (simplicial complexes)\n- New techniques in dimension reduction (circular coordinates, etc.)\n- Clustering (topology-based data partition, classification)\n- Homology and persistent homology\n- Topological signatures for classification\n- Structural inference and reconstruction from data\n- Topological algorithms for massive data\n- Deep learning with TDA\n- Multivariate and high-dimensional data analysis\n- Topological data analysis for visualization (vector fields, topological structures)\n- Practical applications of TDA\n\n🎥 Playlist on YouTube (28 Lectures) : https://youtube.com/playlist?list=PLDZ6LA16SDbIvbgmCjcCuTA7mttfXjiec\u0026si=FiadJKIdmKlJUIY7\n\n\n### Optimal Transport\nProf. Brittany Hamfeldt \n\n![image](https://github.com/user-attachments/assets/13feaab0-c34c-420f-ac97-27312dcca17b)  \n\n🎥 Video Lectures: https://youtube.com/playlist?list=PLJ6garKOlK2qKVhRm6UwvcQ46wK-ciHbl\u0026si=zeG5RCK_E04SRNww\n\n\n### Group Theory\nProf. Richard Borcherds  \n\n![image](https://github.com/user-attachments/assets/4566a212-d4e8-49e6-9ffe-765f60beb12d)\n\n🎥 Lectures: https://www.youtube.com/@richarde.borcherds7998/playlists\n\n\n### Manifold Learning, Optimization and Information Geometry\nPolitecnico di Milano 2022\n\n![image](https://github.com/user-attachments/assets/59fde121-bf22-4d37-93a5-8bcef4a4af80)  \n\n🎥 Lectures: https://youtube.com/playlist?list=PLvVaDdaHGtpesn2DHUo6ete-1pPhT1xzj\u0026si=24WgTbFLChWMaJRx  \n\n\n### Random Matrix Theory\nKing's College London, Dr Pierpaolo Vivo  \n\n![image](https://github.com/user-attachments/assets/61e34a9c-3d6a-4c10-9ef4-bb9941e6a7bf)  \n\n🎥 Lectures : https://www.youtube.com/playlist?list=PLyHAvCibkccQEFYXdM6r8WG4GQULRKmRA\n\n\n### Topological Data Analysis\nColorado State University, Henry Adams, 2021\n\n![image](https://github.com/user-attachments/assets/2da24886-19c9-4db3-9540-ede0b4b7b2e3)  \n\n🎥 Videos (27 short lectures) : https://www.math.colostate.edu/~adams/teaching/dsci475spr2021/\n\n\n### Matrix Calculus for Machine Learning and Beyond\nMIT,  Prof. Alan Edelman, Prof. Steven G. Johnson, 2023 \n\n![image](https://github.com/user-attachments/assets/7f6bff74-51ef-4caa-8f6c-62cd2d02abfe)  \n\n🎥 YouTube (8 lectures): https://youtube.com/playlist?list=PLUl4u3cNGP62EaLLH92E_VCN4izBKK6OE\u0026si=rNoLocGXOEXBQjMH\n\n\n### Probabilistic Machine Learning\nUniversity of Tübingen, Dr. Philipp Hennig, 2023  \n\n![image](https://github.com/user-attachments/assets/ec7dbeee-c289-4c70-962e-40e7922aa206)  \n\n\n- Reasoning Under Uncertainty\n- Continuous Random Variables\n- Exponential Families\n- Gaussian Probability Distributions\n- Parametric Regression\n- Gaussian Processes\n- Understanding Gaussian Processes\n- GP Regression\n- Understanding Kernels and Gaussian Processes\n- The role of Linear Algebra in Gaussian Processes\n- Computation and Inference\n- Logistic Regression\n- GP Classification\n- Deep Learning\n- Probabilistic Deep Learning\n- Uncertainty in Deep Learning\n- Uses of Uncertainty for Deep Learning\n- Gauss-Markov Models\n- Parameter Inference\n- Variational Inference\n\n\n🎥 Lectures (25 lectures): https://youtube.com/playlist?list=PL05umP7R6ij2YE8rRJSb-olDNbntAQ_Bx\u0026si=qivnfDBYjFOu1TOk  \n📔 Slides: https://github.com/philipphennig/Probabilistic_ML  \n\n\n### Discrete Differential Geometry\nCarnegie Mellon Universit  \n\n![image](https://github.com/user-attachments/assets/baf9fc2a-14a8-4627-b42c-651fc81d72f9)\n\n🎥 Lectures : https://www.youtube.com/playlist?list=PL9_jI1bdZmz0hIrNCMQW1YmZysAiIYSSS\n\n### Applied Numerical Algorithms\nMIT, Prof. Justin Solomon, 2023\n\n![image](https://github.com/user-attachments/assets/9dbd7f68-2c58-447b-a93f-f6ee483b7118)\n\n🎥 Lectures : https://www.youtube.com/watch?v=Xt4p5gk24ss\n\n### Shape Analysis \nMIT, Prof. Justin Solomon\n\n![image](https://github.com/user-attachments/assets/3f5e7038-d0ee-48d9-b5ab-e41391c97f41)\n\n\n🎥 Lectures : https://www.youtube.com/watch?v=VjyBp6PrvB4\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Famirabbasasadi%2Fmathematics-computerscience-courses","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Famirabbasasadi%2Fmathematics-computerscience-courses","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Famirabbasasadi%2Fmathematics-computerscience-courses/lists"}