Syllabus Application
Machine Learning
CS 512
Faculty:
Faculty of Engineering and Natural Sciences
Semester:
Fall 2025-2026
Course:
Machine Learning - CS 512
Classroom:
FENS-L063,FMAN-L018
Level of course:
Masters
Course Credits:
SU Credit:3.000, ECTS:10, Engineering:6
Prerequisites:
-
Corequisites:
-
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...). |
|---|---|
| 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
To teach fundamentals of machine learning so that each student can select, implement and evaluate an appropriate machine learning technique for a given problem.
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Course Materials
Resources:
No required textbooks. There will be required readings and videos posted on SuCourse.
Some reference textbooks:
Tom Mitchell, Machine Learning, McGraw Hill, 1997.
Kevin P. Murphy, Machine Learning: A Probabilistic Perspective, MIT Press, 2012.
Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, 2011.
Ethem Alpaydin, Introduction to Machine Learning (2nd ed.), MIT Press, 2010.
Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, An Introduction to Statistical Learning: with Applications in R (online version available).
Andreas C. Müller and Sarah Guido, Introduction to Machine Learning with Python, O’Reilly Media, 2016.
Aurélien Geron, Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques for Building Intelligent Systems, O’Reilly Media, 2017.
For further reading lists, please check: https://machinelearningmastery.com/machine-learning-books/
Some reference textbooks:
Tom Mitchell, Machine Learning, McGraw Hill, 1997.
Kevin P. Murphy, Machine Learning: A Probabilistic Perspective, MIT Press, 2012.
Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, 2011.
Ethem Alpaydin, Introduction to Machine Learning (2nd ed.), MIT Press, 2010.
Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, An Introduction to Statistical Learning: with Applications in R (online version available).
Andreas C. Müller and Sarah Guido, Introduction to Machine Learning with Python, O’Reilly Media, 2016.
Aurélien Geron, Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques for Building Intelligent Systems, O’Reilly Media, 2017.
For further reading lists, please check: https://machinelearningmastery.com/machine-learning-books/