Prof. Abdelkader Baggag
Hamad Bin Khalifa University (HBKU), Qatar
(+974) 44547250
abaggag [at] hbku.edu.qa
Doha, Qatar
This course covers the theory, algorithms, and applications of computational learning. The technical topics covered include linear models, theory of generalization, regularization and validation, neural networks, support vector machines, as well as specialized techniques and a termlong project with big datasets.
1 
The Learning Problem

2 
Theory of Generalization

1 
The Linear Model

2 
Neural Networks and a Peek on Deep Learning

3 
Overfitting & Regularization

4 
Support Vector Machines

5  Kernel Methods: The kernel trick; softmargin SVM. 
6  Radial Basis Functions: RBF and nearest neighbors; RBF and neural networks; RBF and regularization. 
7 
Convex Optimization in Machine Learning

8 
A Peek at Unsupervised Learning

1 
Supervised Learning 
2 
Unsupervised Learning 
3 
Reinforcement Learning 
4 
Active Learning 
5 
Online Learning 
6 
Bayesian Learning 
7 
Graphical Models 
Machine Learning is a subject with a lot of very good expertise and tutorials. It is best to tap on these resources, as they have good production quality and are more condensed. However, we still recommend inclass lectures as they are helpful in building better connection with the materials. Students are highly encouraged to listen to The Video Lectures of Prof. Yaser AbuMostafa.