Course Title

Machine Learning

Course Number

10646544

Instructor Name

Naser Abu-Zaid

Contact Information

[email protected]

Semester(s) and academic year(s)

Second Semester 2023
Second Semester 2022

Compulsory / Elective

Elective

This course aims to provide students with an introduction to Machine Learning (supervised and unsupervised). Design and implement algorithm used for classification and regression. In this course students will learn how Design and implement algorithms used in supervised and unsupervised learning, how to build systems that learn and adapt using examples from real-world applications. The course will cover main algoriths for regression, classification, and clustering. Topics include Classification, CNN's, Linear and logistic regression, regularization, cross validation, nueral networks, etc. Matlab programming and apps will be extensively used and also Python programming language.

• Understand the concept of machine learning.
• Understand the difference between supervised and unsupervised learning.
• Understand the difference between machine learing and deep learning.
• Design and evaluate machine and deep learning algorithms.
• Evaluation of algorithms.
• Improve Programming skills in Matlab and python.

• Develop an appreciation for what is involved in Learning from data
• Understand main learning algorithms
• Understand how to evaluate models generated from data
• Apply the algorithms to a real problem, optimize the models learned and report on the expected accuracy that can be achieved by applying the models

• Artificial Intelligence A Modern Approach, Stuart J. Russell and Peter Norvig, 2020.
• The Hundred page Machine Learning Book, Andriy Burkov,2022.