Course Number 0512-4264-05
Course Name Introduction to Machine Learning
Academic Unit The Iby and Aladar Fleischman Faculty of Engineering -
Electrical Engineering
Mode of Instruction Exercise
Credit Hours 1
Semester 2020/2
Day Thu
Hours 09:00-10:00
Course is taught in English
Syllabus Not Found

Short Course Description

1) Bayesian decision Rules. Discriminant Functions.
2) Parameter estimation using maximum likelihood and using the Bayesian approach.
3) Non-parametric classifiers, non-parametric density estimation. Parzen windows. The nearest neighbors classification rule.
4) Linear models for regression and classification, least squares, regularization, lasso, ridge regression, logistic regression.
5) Maximum margin classification, support vector machines, kernel functions.
6) Neural networks, deep learning, applications.
7) Unsupervised learning, clustering methods, the K means algorithm.
8) The expectation maximization (EM) algorithm, applications to mixture model parameter estimation.
9) Markov and hidden Markov models (HMM), classification and parameter estimation, applications.
10) Principle component analysis (PCA).

Full syllabus will be available to registered students only
Course Requirements

Students may be required to submit additional assignments
Full requirements as stated in full syllabus

PrerequisiteRandom Signals and Noise (05123632)

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