MTH594 Advanced data mining: theory and applications

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 :)

Currently, the following list of topics is covered:

Lecture 1

Basic notations

Linear regression

Gradient descent

Normal equations

Lecture 2

Locally weighted regression

Linear regression: probabilistic interpretation

Logistic regression

Perceptron

Lecture 3

Newton's method

Exponential family

Generalized Linear Models (GLM)

Generative learning algorithms

Lecture 4

Gaussians

Gaussian discriminant analysis

Generative vs Discriminant comparison

Naive Bayes

Laplace Smoothing

Lecture 5

Event models

Neural networks

Support vector machines: intuition

Lecture 6

Primal/dual optimization problem and KKT

SVM dual

Kernels

Soft margin algorithm

SMO algorithm

bkg6/MTH594_MachineLearning