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.
Machine Learning (CS 412)
2025 Fall
Faculty of Engineering and Natural Sciences
Computer Sci.& Eng.(CS)
3
10/6 ECTS (for students admitted in the 2013-14 Academic Year or following years)
Ayşe Berrin Yanıkoğlu berrin@sabanciuniv.edu,
Click here to view.
English
Undergraduate
MATH201 MATH203
Formal lecture,Recitation
Interactive,Learner centered,Project based learning
Click
here
to view.
| Programs\Type | Required | Core Elective | Area Elective |
| Business Analytics Minor | * | ||
| Computer Science and Engineering | * | ||
| Computer Science and Engineering | * | ||
| Data Science and Analytics | * | ||
| Data Science and Analytics | * | ||
| Decision and Behavior Minor | * | ||
| Electronics Engineering | * | ||
| Electronics Engineering | * | ||
| Industrial Engineering | * | ||
| Industrial Engineering (Previous Name: Manufacturing Systems Engineering) | * | ||
| Materials Science and Nano Engineering | * | ||
| Materials Science and Nano Engineering (Previous Name: Materials Science and Engineering) | * | ||
| Mechatronics Engineering | * | ||
| Mechatronics Engineering | * | ||
| Microelectronics | * | ||
| Molecular Biology, Genetics and Bioengineering | * | ||
| Molecular Biology, Genetics and Bioengineering (Pre. Name: Biological Sciences and Bioengineering) | * | ||
| Telecommunications | * |
CONTENT
OBJECTIVE
To teach fundamentals of Machine Learning for students of all backgrounds, so that they will know its capabilities, limitations and be able to design all aspects of a learning system.
Learning Objectives:
1. Understand the basic concepts, issues, assumptions and limitations in machine learning (e.g. base accuracy, overfitting, bias/variance, curse of dimensionality...).
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 tool such as Weka/Matlab or a programming language Python/R.
LEARNING OUTCOMES
- Have a solid understanding of the basic concepts, issues, assumptions and limitations in machine learning and how they apply to various machine learning techniques.
- 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.
- 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.
PROGRAMME OUTCOMES
1. Understand the world, their country, their society, as well as themselves and have awareness of ethical problems, social rights, values and responsibility to the self and to others. 1
2. Understand different disciplines from natural and social sciences to mathematics and art, and develop interdisciplinary approaches in thinking and practice. 1
3. Think critically, follow innovations and developments in science and technology, demonstrate personal and organizational entrepreneurship and engage in life-long learning in various subjects; have the ability to continue to educate him/herself. 3
4. Communicate effectively in Turkish and English by oral, written, graphical and technological means. 3
5. Take individual and team responsibility, function effectively and respectively as an individual and a member or a leader of a team; and have the skills to work effectively in multi-disciplinary teams. 3
1. Possess sufficient knowledge of mathematics, science, fundamental engineering, computational methods and program-specific engineering topics; use theoretical and applied knowledge of these areas in complex engineering problems. 5
2. Identify, define, formulate and solve complex engineering problems while considering the UN Sustainable Development Goals; choose and apply suitable analysis, design, estimation/prediction and modeling methods for this purpose. 5
3. Develop, choose and use modern techniques and tools that are needed for analysis and solution of complex problems faced in engineering applications; use information technologies effectively. 5
4. Have the ability to design a complex system, process, instrument or a product under realistic constraints and conditions, with the goal of fulfilling creative current and future requirements. 4
5. Use research methods, including conducting literature reviews, designing experiments, performing experiments, collecting data, analyzing results, and interpreting results, to investigate complex engineering problems or discipline-specific research topics. 5
6. Possess knowledge of business practices such as project management, risk management, change management, and economic feasibility analysis; awareness on entrepreneurship and innovation. 1
7. Possess knowledge of impact of engineering solutions on society, health and safety, the economy, sustainability, and the environment within the framework of the UN Sustainable Development Goals; awareness on legal outcomes of engineering solutions; awareness of acting impartially and inclusively without any form of discrimination; act in accordance with ethical principles, possessing knowledge of professional and ethical responsibilities. 2
8. Communicate effectively, both orally and in writing, on technical subjects, considering the diverse characteristics of the target audience (such as education, language, and profession). 4
Update Date:
ASSESSMENT METHODS and CRITERIA
| Percentage (%) | |
| Final | 30 |
| Midterm | 30 |
| Quiz | 15 |
| Homework | 25 |
RECOMENDED or REQUIRED READINGS
| Optional Readings |
Textbook: None. Normally slides and lectures will be sufficient to do well in the exam; however, references to sections in one or two of the reference books below will be provided as supplement to lecture slides. Reference Books: |