Foundations of Statistical Learning

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The course provides an introduction to the mathematics of data analysis and a detailed overview of statistical models for inference and prediction. It also introduces new tools being developed in the field of statistics and signal processing for the analysis of data now available in a variety of fields such as in finance, marketing, social networks, and engineering applications. Important regularization methods for modeling and prediction are presented, along with relevant applications. Real-world examples are used to illustrate the methods taught. To reinforce and facilitate the use of the statistical learning techniques, students will explore these in R and MatLab programming experiments.


0. Introduction

1. Probability

2. Stationary processes

3. EigenAnalysis

4. Maximum likelihood

5. Bayes

6. Wiener filtering

7a. Adaptive optimization

7. LS and RLS


9. Logisitic Regression

10. Matrix Completion

11. Linear Models

12. Classification

13. Logistic regression

14. Basis expansions

15. CS 25 minute tour

16. Intro to R