JPMC: 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.







Lectures

Syllabus

1. Probability and Stationary processes

2a. Eigen Analysis, SVD, PCA

2b. Matrix Completion

3. The Learning Problem

4. Training vs Testing

5a. The Linear Model and Optimization

5b. Nonlinear Transformation and Logistic Regression

6. Overfitting and Regularization

7. Lasso Regression

8. Support Vector Machines

8. Neural Networks

Homework

1. Homework 1