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
In this cource we concentrate on the following reference:
Christopher M. Bishop
(2006) Pattern Recognition and Machine
- Probability Distributions
Linear Models for Regression
Linear Basis Function Models
Bayesian Linear Regression
Bayesian Model Comparison
Linear Models for Classification
Probabilistic Generative Models .
Probabilistic Discriminative Models
Bayesian Logistic Regression
Feed-forward Network Functions
The Hessian Matrix
Regularization in Neural
Bayesian Neural Networks
Radial Basis Function Networks
Sparse Kernel Machines
Relevance Vector Machines
Mixture Models and EM
(c) 2013 Meysam Madani 2013