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

Lecture 6

Support vector machines: intuition

Primal/dual optimization problem and KKT

SVM dual

Kernels

Soft margin algorithm

SMO algorithm

Lecture 7

Generalized additive models (GAM)

Tree-based methods

Boosting

Boosting trees

Lecture 8

Bias / variance

Empirical risk minimization (ERM)

Union bound / Hoeffding inequality

Uniform convergence

VC dimension

Lecture 9

Model selection

Feature selection

Bayesian approach and regularization

Online learning

Advices for apply ML algorithms

Lecture 10

Clustering (k-means)

Mixture of Gaussians

Jensen's inequality

General EM algorithm

EM algorithm for the mixture of Gaussians

EM algorithm for the mixture of Naive Bayes

Lecture 11

Factor analysis

Principal component analysis

Latent semantic indexing

Independent component analysis

AwasthiMaddy/MTH594_MachineLearning

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