What Will Learn?

• Course Aims

  • Provide students with introductory background in field of machine learning and its different techniques such as Bayesian theory of decision, classification, clustering, Markov models and artificial neural networks. • Develop the ability of students to apply the basic machine learning techniques to real problems using suitable software packages.

Requirements

ECE465

Description

• English Description
  
  Introduction to machine learning, taxonomy of machine learning, Bayesian theory of decision, Bayes classifier, loss functions, discriminant functions, discriminant functions for Gaussian likelihood, clustering (batch k-means, online k-means, self-organizing maps), Gaussian mixture models, expectation maximization algorithm, hidden Markov models (likelihood problem, decoding problem, learning problem), artificial neural networks, single layer and multilayer neural networks, neural network training.
• Arabic Description
  
  Introduction to machine learning, taxonomy of machine learning, Bayesian theory of decision, Bayes classifier, loss functions, discriminant functions, discriminant functions for Gaussian likelihood, clustering (batch k-means, online k-means, self-organizing maps), Gaussian mixture models, expectation maximization algorithm, hidden Markov models (likelihood problem, decoding problem, learning problem), artificial neural networks, single layer and multilayer neural networks, neural network training.