Resources
Slides and coding scripts would be distributed after each lecture via the Google groups mailing-list. Refer to the home page on how to subscribe.
There are amazing resources available online, which the course would refer to, leverage and point at for delving into more details. Here are a few initial pointers. More to come in classes..
Course book references
For Data Science:
Bertsimas, O'Hair, Pulleyblank. The Analytics Edge. From the editor or on Amazon.
Jure Leskove, Anand Rajaraman, Jeffrey D. Ullman, 2019. Mining of Massive Datasets. Cambridge University Press. (available at: http://infolab.stanford.edu/~ullman/mmdsn.html)
For Machine Learning:
Christopher M. Bishop, 2006. Pattern Recognition and Machine Learning
For Deep Learning:
Ian Goofellow, Yoshua Bengio, Aaron Courville, 2017. Deep Learning (available at: https://www.deeplearningbook.org/)
Andrew Ng, 2019. Machine Learning Yearning (available at: https://www.deeplearning.ai/machine-learning-yearning/)
For Image Analysis and Computer Vision:
Richard Szeliski, 2010. Computer Vision: Algorithms and Applications (available at: http://szeliski.org/Book)
Other recommended books:
Kleinberg, Tardos. Algorithm Design. Addison Wesley.
Boyd, Vandenberghe. Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares. Cambridge University Press.
Coding references
Coding examples and assignments would be in Python (3.x), leveraging the Pytorch (1.x) framework.
Book reference for Python:
Allen B. Downey, 2015. Think Python: How to Think Like a Computer Scientist (available at: https://www.greenteapress.com/thinkpython/thinkpython.html)
Jake VanderPlas, 2016. Python Data Science Handbook: Tools and Techniques for Developers: Essential Tools for working with Data (Book and notebooks available at: https://github.com/jakevdp/PythonDataScienceHandbook)
Online tutorials for Python:
https://docs.python.org/3/tutorial/
Online tutorials for Pytorch:
https://pytorch.org/tutorials/
Setup and computing
A Linux OS is recommended, although most Python and Pytorch distributions also run on Windows.
A nice Python distribution (both Linux and Windows) is Anaconda, which includes the simple and straightforward Spyder IDE:
https://www.anaconda.com/distribution/
For running some exercises you may need a GPU. A nice resource for computational resources, available for free, is Google colab:
https://colab.research.google.com
You may refer to this tutorial on how to setup Pytorch in Google colab:
https://medium.com/dair-ai/pytorch-1-2-quickstart-with-google-colab-6690a30c38d