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Syllabus

Course Number 0368-3235-05
Course Name Introduction to Machine Learning
Academic Unit The Raymond and Beverly Sackler Faculty of Exact Sciences -
Computer Science
Mode of Instruction Exercise
Credit Hours 1
Semester 2024/1
Day Wed
Hours 17:00-18:00
Building Kaplun - Physics
Room 118
Course is taught in English
Syllabus Not Found

Short Course Description

This course provides a basic introduction to machine learning (namely the field of computer science studying algorithms that learn from examples). This field underlies modern applications of AI, including machine vision, natural language processing, autonomous driving and others.
The course will provide the theoretical foundations for understanding learning algorithms, describe different algorithms, and provide practical experience.
Some of the topics studies are:
+ Supervised learning: PAC learning, VC dimension, perceptron, SVM, stochastic gradient descent, boosting, deep learning and decision trees
+ Unsupervised learning: principal component analysis, clustering, EM algorithm

The course comprises both theory and practice, with special emphasis on different machine learning algorithms. Theoretical topics are mathematical in nature; practical exercises are based on Python.



Full Syllabus
Course Requirements

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

PrerequisiteIntroduction to (03662010) ORProbability And (03682002) ORStatistics For (03652301) ORProbability for Sciences (03652100) ORIntr. to Probability and (05092801) ORIntroduction To (03651101) ORProbability And (03211836) Parallel coursesAlgorithms (03682160) ORData Structures and Algor (05122510)

The specific prerequisites of the course,
according to the study program, appears on the program page of the handbook



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