Courses
PRLM Links
- Machine Learning, Tom Mitchell book Supplements
- Pattern Recognition and Machine Learning, C. Bishop book Supplements
Machine learning
Machine learning, a branch of artificial intelligence, concerns the construction and study of systems that can learn from data.
Course Materials
- Syllabus of the course, In Persian.
- Template for Proposal, Fill and send it just once.
- Introduction to Scilab, By Michael Baudin.
- Slides for Chapter 4., This is a Draft, Please read the book.
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!
Deadlines:
- 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
Radial Basis Function Networks
Gaussian Processes
Sparse Kernel Machines
Maximum Margin Classifiers
Relevance Vector Machines
Graphical Models
Mixture Models and EM
Conditional Independence