Selected topics in Data Science and Machine Learning (RadFarm) (2020/2021)

Course organization

• place: virtual meetings ;)
• time: to be decided
• info and materials: this page
• format: lectures and classes

Lectures

Additional materials

Articles and demos

• TensorFlow Demo
• ROC curve applet
• Some Studies in Machine Learning using the Game of Checkers - Arthur Samuel (1959)
• AI, Deep Learning, and Machine Learning: A Primer - Frank Chen
• Building high-level features using large scale unsupervised learning - Quoc V. Le et al. (2012)
• Principles of Risk Minimization for Learning Theory - V. Vapnik
• An overview of Statistical Learning Theory - V. Vapnik (2000)
• General conditions for predictivity in learning theory - T. Poggio et al., (2004)
• Statistical Modeling: The Two Cultures - L. Breiman
• Graphs in Statistical Analysis -F. J. Anscombe (1973). See also the local copy here
• On Discriminative vs Generative classifiers - a comparison of logistic regression and naive Bayes -A. Ng and M. I. Jordan See also the local copy here
• The Optimality of Naive Bayes - H. Zhang
• An introduction to ROC analysis - Tom Fawcett (2005)
• The relationship between Precision-Recall and ROC curves - J. Davis and M. Goadrich
• The Unreasonable Effectiveness of Data - A. Halevy, P. Norvig, and F. Pereira

Programming

In general programming in Python is easy and one can start very quickly. Of course some previous experience in programming is very helpful. For our course except from the standard Python, it would be useful to learn a bit about numpy and matplotlib.

• Official Python community website
• A lot of tutorials
• Dive into Python. I would recommend this book for people who feel that they have some experience in programming.
• Numpy introduction
• Matplotlib
• scikit-learn
• Python style guide

Datasets