закладки (спрятать)627
- Introduction to Theoretical Computer Science
- Research Topics about Bayesian Inference
- Statistical Learning Theory Resources and Material
- Great Reading List on Topics and Aspects about Computation and Machine Learning
- Graphical Models Course CMU 2014
- Introduction to Neural Networks Course 2015
- Bayesian Non-Parametric Models at NIPS 2009
- Maxim Raginsky - Courses on Statistical Learning Theory and Random Processes
- Deep Learning Tutorials Toronto
- Blog posts about ML and pytorch
- Probability, The Analysis of Data Volume 1
- Foundations of Machine Learning -- CSCI-GA.2566-001
- MIT OCW SLT Spring 2007
- Earliest Known Uses of Some of the Words of Mathematics (B)
- Deep Learning Theory (CS 598 DLT)
- Mathematical Proofs
- Handbook of Graphical Models by Statisticians
- Emacs Config
- Nice blog post notes on ML and convex optimization
- Stats and Matrix Calculus Course
- Graphical Models Course
- Machine learning with kernel methods, 2019
- Workshop II: Interpretable Learning in Physical Sciences (Schedule) - IPAM
- Configuring Emacs, lsp-mode and Microsoft's Visual Studio Code Python language server. - vxlabs
- How to setup Emacs to use LSP with C++, Python, and Rust
- Blog about R and Python
- Uncertainty Lecture Slides
- A great presentation and tutorial about kernels and everything you'll ever wanted to know about them!
- Properties of random variables and probability in BioStatistics course at Duke
- Calculus course OCW MIT
- Algebraic Geometry Book
- Textbook | Calculus Online Textbook | MIT OpenCourseWare
- Sociology Graduate Stats I
- History of Approximation Theory
- Probability and Stats 620 - Edward Ionides
- Probabilistic Graphical Models, Fall 2007
- Probabilistic Graphical Models
- Stanford University CS236: Deep Generative Models
- This course introduces students to probability and random variables. Topics include distribution functions, binomial, geometric, hypergeometric, and Poisson distributions. The other topics covered are uniform, exponential, normal, gamma and beta distributions; conditional probability; Bayes theorem; joint distributions; Chebyshev inequality; law of large numbers; and central limit theorem.
- This course explores electromagnetic phenomena in modern applications, including wireless and optical communications, circuits, computer interconnects and peripherals, microwave communications and radar, antennas, sensors, micro-electromechanical systems, and power generation and transmission. Fundamentals include quasistatic and dynamic solutions to Maxwell's equations; waves, radiation, and diffraction; coupling to media and structures; guided waves; resonance; acoustic analogs; and forces, power, and energy.
- The Modern Algorithmic Toolbox By Gregory Valiant
- Statistical Learning Theory Course from Tengyu Ma
публикации (спрятать)2111
- (2019)
- (2020)cite arxiv:2002.11803.
- (2020)cite arxiv:2002.01523.
- (2016)cite arxiv:1602.03943.
- (2019)cite arxiv:1902.08721.
