| Industrial Engineering (English) | |||||
| Bachelor | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF-LLL: Level 6 | ||
| Course Code: | COE305 | ||||
| Course Name: | Machine Learning | ||||
| Semester: | Fall | ||||
| Course Credits: |
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| Language of instruction: | English | ||||
| Course Condition: | |||||
| Does the Course Require Work Experience?: | No | ||||
| Type of course: | Compulsory Courses | ||||
| Course Level: |
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| Mode of Delivery: | Face to face | ||||
| Course Coordinator: | Doç. Dr. AMIR SEYYEDABBASI | ||||
| Course Lecturer(s): | Assist. Prof. Dr. Femilda Josephin Joseph Shobana Bai | ||||
| Course Assistants: |
| Course Objectives: | This course will cover the fundamentals of Machine Learning, including supervised and unsupervised learning, classification, regression, clustering, decision trees, and basics of deep learning. Students will learn how to apply these techniques to solve real-world problems, such as image classification, natural language processing, and recommender systems. Additionally, the course will cover the concepts of model evaluation, feature selection, and data preprocessing, which are crucial for developing effective Machine Learning models. |
| Course Content: | The content of the course consists of linear regression, logistic regression, K-Nearest Neighbor, Support Vector Machines, Naïve Bayes, K- means clustering, Principal component analysis, ensemble learning algorithms like the random forest, and gradient boosting machines, artificial neural networks with single-layer and multiple-layer perceptrons |
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The students who have succeeded in this course;
1) Understand the principles and techniques of supervised and unsupervised learning, classification, regression, clustering, decision trees, and basics of deep learning. 2) Apply Machine Learning techniques to solve real-world problems, such as image classification, natural language processing, and recommender systems. 3) Evaluate and select appropriate models for given problem statements using the concepts of model evaluation and feature selection. 4) Preprocess data to prepare it for Machine Learning modeling. 5) Analyze and interpret Machine Learning model outputs to draw insights and make informed decisions. |
| Week | Subject | Related Preparation |
| 1) | Introduction to Machine Learning | |
| 2) | Supervised Learning-Regresion Problem | |
| 3) | Supervised Learning - Classification Problem - Logistic Regression - K-Nearest Neighbor | |
| 4) | Exploratory Data Analysis Data Pre-processing | |
| 5) | Supervised Learning - Decision Tree - Handling imbalanced dataset | |
| 6) | Supervised Learning - Random Forest - Cross Validation and its types | |
| 7) | Supervised Learning - Naive Bayes and Support Vector Machines | |
| 8) | Midterm Exam | |
| 9) | Supervised Learning - Hyper parameter tuning Performance metrics | |
| 10) | Unsupervised Learning | |
| 11) | Ensemble Learning Methods | |
| 12) | Dimensionality Reduction | |
| 13) | Artificial Neural Networks Perceptrons | |
| 14) | Artificial Neural Networks - Multi-layer Networks |
| Course Notes / Textbooks: | 1) Introduction to Machine Learning, Ethem Alpaydın (3rd Edition), 2014, MIT Press 2) Understanding Machine Learning: From Theory to Algorithms, Shai Shalev-Shwartz, Shai Ben-David (1st Edition), 2014, Cambridge University Press 3) Pattern Recognition and Machine Learning, Christopher Bishop (1st Edition), 2006, Springer 4) Learning from Data, Yaser S. Abu-Mostafa, Malik Magdon-Ismail, Hsuan-Tien Lin (1st Edition), 2012, AMLBook |
| References: | "1) Linear Algebra and Optimization for Machine Learning: A Textbook, Charu C. Aggarwal (1st Edition), 2020, Springer Press 2) Optimization for Machine Learning, Suvrit Sra, Sebastian Nowozin, Stephen J. Wright (Edited - 1st Edition), 2011, MIT Press" |
| Course Learning Outcomes | 1 |
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| Program Outcomes | |||||||||||
| 1) Adequate knowledge in mathematics, science and industrial engineering; the ability to use theoretical and practical knowledge in these areas in complex engineering problems. | 2 | 2 | 2 | 2 | 2 | ||||||
| 2) Ability to identify, formulate, and solve complex industrial engineering problems; ability to select and apply appropriate analysis and modeling methods for this purpose. | 2 | 2 | 2 | 2 | 2 | ||||||
| 3) Ability to design a complex industrial system, process, device or product to meet specific requirements under realistic constraints and conditions; ability to apply modern design methods for this purpose. | |||||||||||
| 4) Ability to develop, select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in industrial engineering applications; ability to use information technologies effectively. | 2 | 2 | 2 | 2 | 2 | ||||||
| 5) Ability to design, conduct experiments, collect data, analyze and interpret results for the study of complex engineering problems or industrial engineering research topics. | 2 | 2 | 2 | 2 | 2 | ||||||
| 6) Ability to work effectively within and multidisciplinary teams; individual study skills. | |||||||||||
| 7) Ability to communicate effectively orally and in writing; knowledge of at least one foreign language; ability to write effectice reports and understand written reports, to prepare design and production reports, to make effective presentations, to give and receive clear and understandable instructions. | |||||||||||
| 8) Awareness of the necessity of lifelong learning; ability to access information, to follow developments in science and technology and to renew continuously. | |||||||||||
| 9) To act in accordance with ethical principles, professional and ethical responsibility; information on the standards used in engineering applications. | |||||||||||
| 10) Information on business practices such as project management, risk management and change management; awareness of entrepreneurship and innovation; information about sustainable development. | |||||||||||
| 11) Knowledge of the effects of industrial engineering practices on health, environment and safety in the universal and social scale and the problems of the era reflected in industrial engineering; awareness of the legal consequences of industrial engineering solutions. | |||||||||||
| No Effect | 1 Lowest | 2 Average | 3 Highest |
| Program Outcomes | Level of Contribution | |
| 1) | Adequate knowledge in mathematics, science and industrial engineering; the ability to use theoretical and practical knowledge in these areas in complex engineering problems. | 2 |
| 2) | Ability to identify, formulate, and solve complex industrial engineering problems; ability to select and apply appropriate analysis and modeling methods for this purpose. | 2 |
| 3) | Ability to design a complex industrial system, process, device or product to meet specific requirements under realistic constraints and conditions; ability to apply modern design methods for this purpose. | |
| 4) | Ability to develop, select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in industrial engineering applications; ability to use information technologies effectively. | 2 |
| 5) | Ability to design, conduct experiments, collect data, analyze and interpret results for the study of complex engineering problems or industrial engineering research topics. | 2 |
| 6) | Ability to work effectively within and multidisciplinary teams; individual study skills. | |
| 7) | Ability to communicate effectively orally and in writing; knowledge of at least one foreign language; ability to write effectice reports and understand written reports, to prepare design and production reports, to make effective presentations, to give and receive clear and understandable instructions. | |
| 8) | Awareness of the necessity of lifelong learning; ability to access information, to follow developments in science and technology and to renew continuously. | |
| 9) | To act in accordance with ethical principles, professional and ethical responsibility; information on the standards used in engineering applications. | |
| 10) | Information on business practices such as project management, risk management and change management; awareness of entrepreneurship and innovation; information about sustainable development. | |
| 11) | Knowledge of the effects of industrial engineering practices on health, environment and safety in the universal and social scale and the problems of the era reflected in industrial engineering; awareness of the legal consequences of industrial engineering solutions. |
| Semester Requirements | Number of Activities | Level of Contribution |
| Quizzes | 2 | % 10 |
| Homework Assignments | 2 | % 10 |
| Project | 1 | % 10 |
| Midterms | 1 | % 30 |
| Final | 1 | % 40 |
| total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 60 | |
| PERCENTAGE OF FINAL WORK | % 40 | |
| total | % 100 | |
| Activities | Number of Activities | Preparation for the Activity | Spent for the Activity Itself | Completing the Activity Requirements | Workload | ||
| Course Hours | 13 | 5 | 65 | ||||
| Application | 1 | 5 | 5 | ||||
| Project | 1 | 4 | 4 | 8 | |||
| Homework Assignments | 2 | 12 | 24 | ||||
| Quizzes | 2 | 8 | 16 | ||||
| Midterms | 1 | 7 | 7 | ||||
| Final | 1 | 13 | 13 | ||||
| Total Workload | 138 | ||||||