Slides, Codes, and Resources for Large-scale Optimization and Its Applications in Machine Learning
- Large-scale Optimization for Machine Learning Lectures by Professor Razaviyayn
- Learning iteration complexity, optimal first-order methods for convex problems (smooth and non-smooth), and semi-definite programming: Introductory Lectures On Convex Programming
- For understanding different types of errors (Bayes error, Estimation Error, Approximation Error, and Optimization Error) check out this awesome paper from ICML 2008: Understanding Different Types of Error in Machine Learning
- Robust PCA (The original paper by Candes): Robust PCA
- An awesome reference for the application of sparsity in statistical learning: Statistical Learning with Sparsity (The Lasso and Generalizations)
- Netflix Prize Problem: The Netflix Prize and Singular Value Decomposition
- Optimization Algorithms for Big Data (Block Coordinate Descent, ADMM): A Unified Algorithmic Framework for Block-Structured Optimization Involving Big Data
- Theory of Probability (Rigorous Mathematical Introduction to Probability): Lecture Notes by Professor Heilman
- Theory of Point Estimation: See Chapters 1-5 Book.
- Hypothesis Testing: A detailed book for a deep understanding of hypothesis testing Book.
- Introduction to Statistical Inference, Hypothesis Testing, and Regression (A summary of above references): [Statistical Inference].(http://home.ustc.edu.cn/~zt001062/MaStmaterials/George%20Casella&Roger%20L.Berger--Statistical%20Inference.pdf).
- An Introduction to Statistical Learning: A beginner yet essential book to enter the statistical learning universe Book.
- Modern Topics of Statistics (especially for high-dimensional settings): High Dimensional Probability.