The materials for the course MTH 594 Advanced data mining: theory and applications taught by Dmitry Efimov in American University of Sharjah, UAE. I teach this course this semester and by June, 2016 I will upload all lectures and supplementary files here. The program of the course can be downloaded from the folder syllabus.
To compose this lectures mainly I used the ideas from three sources:
- Stanford lectures by Andrew Ng on YouTube: https://www.youtube.com/watch?v=UzxYlbK2c7E&list=PLA89DCFA6ADACE599 (lectures 1-6, 8-11)
- The book "The elements of Statistical Learning" by T. Hastie, R. Tibshirani and J. Friedman: http://statweb.stanford.edu/~tibs/ElemStatLearn (lecture 7)
- Lectures by Andrew Ng on Coursera: https://www.coursera.org/learn/machine-learning (lecture 5)
All uploaded pdf lectures are adapted in a way to help students to understand the material.
The supplementary files from ipython folder are aimed to teach students how to use built-in methods to train the models on Python 2.7.
In case you found some mistakes or typos, please email me diefimov@gmail.com, this course is a new for me and probably there are some :)