Books
There is no required text for the course. But here are some useful references:
- Richert, Coelho: Building Machine Learning Systems with Python
- Garetta, Moncecchi: Learning scikit-learn: Machine Learning in Python
- Tom Mitchell, Machine Learning
- Richard Duda, Peter Hart and David Stork, Pattern Classification
- Trevor Hastie, Robert Tibshirani and Jerome Friedman, The Elements of Statistica Learning
- Bishop: Neural Networks for Pattern Recognition
- Cristianini, Taylor: An Introduction to Support Vector Machines and Other Kernel-basedLearning Methods
- Witten, Frank, Hall: Data Mining: Practical Machine Learning Tools and Technique
- Pyle: Data Preparation for Data Mining
- Kevin Murphy, Machine learning: a probabilistic perspective.
- Introduction to Probability
- Gilbert Strang, Linear algebra and its applications.
Online resources
- Pandas
- Scikit-learn: Machine Learning in Python
- API design for machine learning software: experiences from the scikit-learn project
- Quick Start Tutorial
- User Guide
- API Reference
Software
- iPython Notebook (Python version 3.6 preferred)
Python Libraries
- NumPy
- pandas
- scikit-learn
- Matplotlib