These resources are listed here in case you want to explore the topics further. The section on programming is important if you are not familiar with Python, or with installing Python libraries. The section on LaTeX is meant to be a guide for typesetting professionally formatted reports and papers.

*S. Shalev-Shwartz and S. Ben-David,***Understanding Machine Learning**, Cambridge University Press, 2014.**Several notes for this class are adapted from this wonderful book.**- I. Goodfellow, Y. Bengio, A. Courville,
*Deep Learning*, MIT Press 2016 - C. M. Bishop,
*Pattern Recognition and Machine Learning*, Springer 2006 - T. Hastie, R. Tibshirani, and J. Friedman,
*The Elements of Statistical Learning — Data Mining, Inference, and Prediction*, Springer 2001 - K. P. Murphy,
*Machine Learning — A Probabilistic Perspective*, MIT Press 2012 - S. J. Dickinson, A. Leonardis, B. Schiele, and M. J. Tarr, editors,
*Object Categorization — Computer and Human Vision Perspectives*, Cambridge 2009

- Anaconda,
*a stable and coherent distribution of Python for data science. It is*This distribution places all relevant files in the appropriate places, and you won't have to struggle with linking libraries, etc. Once you installed Anaconda, run the Anaconda Navigator and familiarize yourself with the tools. Pay attention in particular to the**strongly recommended**that you uninstall any version of Python 3 you may have on your computer and install the Anaconda version 3.6 (or later, if available). This distribution includes Python, a Python editor (Spyder), several basic libraries for data science (numpy, scipy, and more), visualization libraries (including matplotlib), machine learning libraries, including scikit-learn.**This is essentially all you need for this course.**`Jupiter notebook`launcher, as you will submit homework as Jupiter notebooks. - Python 3 for programmers is a note written for this class and meant to quickly introduce you to Python 3 if you already know a programming language well. If you do not, this course is not for you.
- Google's Python class is a leisurely but clear Python tutorial.
- The official Python 3 Documentation also includes a tutorial. Use the library reference and the language reference as your official sources of information about Python 3. You can also find information by googling, but
*make sure you refer to version 3 of Python*if you do so. - Several tutorials on Jupyter notebooks can be found online. Here is one from Dataquest.

- PMTK3, a probabilistic modeling toolkit for Matlab/Octave that accompanies Kevin Murphy's book on machine learning
- LIBSVM, a C++ and Java software library for support vector machines developed by Chih-Chung Chang and Chih-Jen Lin
- SVM light, a C software library for support vector machines developed by Thorsten Joachims
- PyTorch, a deep learning framework in Python developed by the Facebook AI Research Team.
- TensorFlow, a popular deep learning framework with interfaces in several programming languages, developed by the Google Brain Team.

- The Wikipedia entry on machine learning. The Wikipedia has specific entries for most ML topics as well.
- A list of tutorials on machine learning topics maintained by the International Association for Pattern Recognition
- A tutorial on deep learning by the Theano Development Team (see also the original Theano paper)
- A deep learning tutorial by Stanford's Deep Learning Group

- The MNIST database of handwritten digits developed by Yann LeCun, Corinna Cortes, and Christopher J. C. Burges
- The CIFAR-10 dataset of 60,000 images in ten classes developed by Alex Krizhevsky
- The Caltech 101 collections of images in 101 categories developed by Fei-Fei Li, Rob Fergus, and Pietro Perona
- The Caltech 256 collections of images in 256 categories developed by Greg Griffin, Alex Holub, and Pietro Perona

- LaTeX installation instructions for several platforms can be found in the class notes on LaTeX.
- Movie tutorials on LaTeX can be found in this zip file.
- A quick LaTeX tutorial can be found here, and the quick start version on the same site is an even faster, one-page introduction, and is required reading.
- The LaTeX Wikibook is an authoritative online reference to LaTeX.

COMPSCI 371D, Duke University, Site based on the fluid 960 grid system