Analytics I: 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.



1. Review of Probability

2. Stationary processes

3. Eigen Analysis, SVD and PCA

4. The Learning Problem

5. Training vs Testing

6. The Wiener Filter

7. Adaptive Optimization (Steepest Descent and LMS Algorithms)

8. Nonlinear Transformation and Logistic Regression

9. Overfitting and Regularization

10. Ridge and Lasso Regression

11. Neural Networks

12. Matrix Completion

13. Convolutional Neural Networks