## Machine learning

Machine learning, a branch of artificial intelligence, concerns the construction and study of systems that can learn from data.

## Homeworks: (In Scilab)

• 1- Denoise an 200*200 image by histogram or sigmoid!
• 2- Choose some functions and linear coefficients to fit and generate a dataset with some noise on it, then find linear coefficients!
• 3-Generate a linear separable dataset in three class, then use fisher and least square method to find discriminant functions, plot all task you do.
• 4- Please generate a two classes dataset with outlier, then use logistic, least square and fisher method for classifing data. Plot all you do. Also do all before for 4 classes data with outliers, which is not pitza like!

• Project Proposal 10 March 2014
• Midterm 3 times,
•    1- 13 March 2014,
•    2- 4 May 2014,

Chpater 1 and 2 (Distributions, Likelihood and applications),

chapter 3 (regression, model selection, Scilab programming, lambda effects on loss functions, Bayesian Linear Regression, Predictive Distribution), chapter 4 (all i taught).

•    3- 25 May 2014
• Project 17 June 2014
• Final Exam 17 June 2014

## Pattern Recognition and Machin Learning(PRMLML)

Machine learning, a branch of artificial intelligence, concerns the construction and study of systems that can learn from data. For example, a machine learning system could be trained on email messages to learn to distinguish between spam and non-spam messages. After learning, it can then be used to classify new email messages into spam and non-spam folders.

The core of machine learning deals with representation and generalization. Representation of data instances and functions evaluated on these instances are part of all machine learning systems. Generalization is the property that the system will perform well on unseen data instances; the conditions under which this can be guaranteed are a key object of study in the subfield of computational learning theory.

In this cource we concentrate on the following reference:

Christopher M. Bishop (2006) Pattern Recognition and Machine Learning, Springer

## Course outline:

• Scilab Programming
• Probability Distributions
Linear Models for Regression

Linear Basis Function Models
Bayesian Linear Regression
Bayesian Model Comparison
Linear Models for Classification
Discriminant Functions
Probabilistic Generative Models .
Probabilistic Discriminative Models
Bayesian Logistic Regression
Neural Networks
Feed-forward Network Functions
Network Training
Error Backpropagation
The Hessian Matrix
Regularization in Neural Networks
Bayesian Neural Networks
Kernel Methods
Dual Representations