Syllabus Application
Machine Learning
CS 412
Faculty:
Faculty of Engineering and Natural Sciences
Semester:
Fall 2025-2026
Course:
Machine Learning - CS 412
Classroom:
FENS-G077
Level of course:
Undergraduate
Course Credits:
SU Credit:3.000, ECTS:6, Basic:2, Engineering:4
Prerequisites:
( MATH 201 and MATH 203)
Corequisites:
CS 412R
Course Type:
Lecture
Instructor(s) Information
Ayşe Berrin Yanıkoğlu
- Email: berrin@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 (overfitting, bias/variance), together with several state-of-art approaches such as decision trees, linear regression, k-nearest neighbor, Bayesian classifiers, support vector machines, neural networks, logistic regression, and classifier combination.
Course Learning Outcomes:
| 1. | Have a solid understanding of the basic concepts, issues, assumptions and limitations in machine learning and how they apply to various machine learning techniques. |
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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, select, implement and evaluate one of the appropriate 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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