bookmarks (hide)627
- Stats and Matrix Calculus Course
- Graphical Models Course
- Nice blog post notes on ML and convex optimization
- Workshop II: Interpretable Learning in Physical Sciences (Schedule) - IPAM
- Machine learning with kernel methods, 2019
- 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
- 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
- History of Approximation Theory
- 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
- Probability, The Analysis of Data Volume 1
- Blog posts about ML and pytorch
- Sociology Graduate Stats I
- 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.
- Gaussian Processes: From the Basics to the State-of-the-Art
- Tutorial on PAC Bayes NIPS 2017
- Advanced Topics in Machine Learning Course by Arthur Gretton http://www.gatsby.ucl.ac.uk/~gretton/coursefiles/
- The Modern Algorithmic Toolbox By Gregory Valiant
- Machine Learning Reading Group & Talks @ Cambridge
- Fall 2014 Linear Systems Course
- Deep Learning Theory Class & Approximation by Matus Telgarsky
- Computer Science 294: Practical Machine Learning by Michael Jordan
- Emacs Config dotfiles
- Deep Learning in Computer Vision Course
- Statistical Learning Theory Course from Tengyu Ma
- MIT Course on Statistical Learning Fall 2019
- Seminar - Fall 2018 Category Theory
- Old and New Matrix Algebra Useful for Statistics
- University of Washington. Research interests: Machine Learning, Artificial Intelligence, Optimization, Statistics
- Probabilistic Models
- Probabilistic Modelling and Bayesian Inference MLSS Tuebingen 2013
- Probability and Stats 620 - Edward Ionides
- Probabilistic Graphical Models, Fall 2007
