Syllabus Application
Machine Learning
CS 512
Faculty:
Faculty of Engineering and Natural Sciences
Semester:
Fall 2025-2026
Course:
Machine Learning - CS 512
Classroom:
FENS-L045,FMAN-L018
Level of course:
Masters
Course Credits:
SU Credit:3.000, ECTS:10, Engineering:6
Prerequisites:
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Corequisites:
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Course Type:
Lecture
Instructor(s) Information
Öznur Taştan Okan
- Email: otastan@sabanciuniv.edu
Course Information
Catalog Course Description
This is an introductory machine learning course that will aim a solid understanding of the fundamental issues in machine learning together with several ML techniques such as decision trees, k-nearest neighbor, Bayesian classifiers, neural networks, linear and logistic regression, clustering, SVM and ensemble techniques.
Course Learning Outcomes:
| 1. | Understand the basic concepts, issues, assumptions and limitations in machine learning (e.g. overfitting, error measures, inductive bias...). |
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| 2. | Have a working knowledge of the basic mathematics (probability, expectation, entropy, basic linear algebra, ...) and algorithms behind common machine learning techniques; together with their suitability in given situations. |
| 3. | Given a machine learning problem, be able to implement and evaluate one of the standard machine learning algorithms (e.g. decision trees, neural networks, SVMs) using a programming environment such as Weka or Matlab. |
Course Objective
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Course Materials
Resources:
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Technology Requirements:
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