Analytics I: Statistical Learning

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The course provides an introduction to the mathematics of data analysis and machine learning. 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 machine learning techniques, students will explore these in TensorFlow and Keras programming experiments. The slides of this course were adapted from Caltech and MIT Machine Learning courses.



Lectures

Syllabus

1. Probability and Stationary processes

2. Eigen Analysis, SVD, PCA and 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

9. Neural Networks

10. Convolutional Neural Networks

11. Deep Autoencoders and Generative models

12. Epilogue