Bought:

• Wasserman: all of statistics
• Hastie: The elements of statistical learning
• Bishop: Pattern recognition and machine learning
• Rosenthal: A first look at rigorous probability theory
• Raschka: Python machine learning

• look at http://karlrosaen.com/ml/learning-log
• do a trial couple of days, see how time goes

Content

Python ML

1. Machine Learning - Giving Computers the Ability to Learn from Data
2. Training Machine Learning Algorithms for Classification
3. A Tour of Machine Learning Classifiers Using Scikit-Learn
4. Building Good Training Sets – Data Pre-Processing
5. Compressing Data via Dimensionality Reduction
6. Learning Best Practices for Model Evaluation and Hyperparameter Optimization
7. Combining Different Models for Ensemble Learning
8. Applying Machine Learning to Sentiment Analysis
9. Embedding a Machine Learning Model into a Web Application
10. Predicting Continuous Target Variables with Regression Analysis
11. Working with Unlabeled Data – Clustering Analysis
12. Implementing a Multi-layer Artificial Neural Network from Scratch
13. Parallelizing Neural Network Training with TensorFlow
14. Going Deeper: The Mechanics of TensorFlow
15. Classifying Images with Deep Convolutional Neural Networks
16. Modeling Sequential Data Using Recurrent Neural Networks

Wasserman

1. Probability
2. Random variables
3. Expectation
4. Inequalities
5. Convergence of Random Variables

TODO - http://www.stat.cmu.edu/~larry/=stat325.02/
TODO- find CMU homework assignments mentioned by Karl

Stanford
TODO