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| BILB3000 | Machine Learning | 4+0+0 | ECTS:6 | | Year / Semester | Spring Semester | | Level of Course | First Cycle | | Status | Compulsory | | Department | COMPUTER SCIENCE | | Prerequisites and co-requisites | None | | Mode of Delivery | | | Contact Hours | 14 weeks - 4 hours of lectures per week | | Lecturer | Dr. Öğr. Üyesi Buğra Kaan TİRYAKİ | | Co-Lecturer | | | Language of instruction | Turkish | | Professional practise ( internship ) | None | | | | The aim of the course: | | The aim of this course is to introduce students to the concept of machine learning and different learning methods. At the end of this course, the student will learn which machine learning method is most appropriate for a real-life problem and how to analyse this method in terms of error and complexity. |
| Learning Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | LO - 1 : | Have knowledge about machine learning techniques. | 2,9 | 1,4, | | LO - 2 : | Can model real world problems with classification. | 2,9 | 1,4, | | LO - 3 : | To be able to solve classification problems of various disciplines with machine learning techniques. | 2,9 | 1,4, | | LO - 4 : | Interpret the solutions obtained with machine learning techniques. | 2,9 | 1,4, | | CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), LO : Learning Outcome | | |
| Introduction to machine learning, sampling learning, multivariate models and regression, model degree and generalisation properties, k-means clustering algorithm, decision trees, Bayesian decision theory, artificial neural networks, support vector machines, dimension reduction and principal component analysis. |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Introduction to machine learning | | | Week 2 | Hierarchical clustering algorithms | | | Week 3 | Piecewise clustering algorithms | | | Week 4 | Nearest neighbour classification | | | Week 5 | Naive Bayes Classification | | | Week 6 | Decision tree classification | | | Week 7 | Polynomial regression | | | Week 8 | Generalised regression | | | Week 9 | Midterm Exam | | | Week 10 | Logistic Regression | | | Week 11 | Support Vector Machines | | | Week 12 | Artificial neural networks | | | Week 13 | Convolutional Neural Networks | | | Week 14 | Dimension reduction | | | Week 15 | Principal component analysis | | | Week 16 | End-of-term exam | | | |
| 1 | Geron, Aurelien, Scikit- Learn Keras ve TensorFlow ile Uygulamalı Makine Öğrenmesi, Buzdağı Yayınevi, 2021, Ankara | | | |
| 1 | Ian Goodfellow, Aaron Courville, Yoshua Bengio, Derin Öğrenme, Buzdağı Yayınevi, 2020, Ankara | | | 2 | François Chollet, Python ile Derin Öğrenme, Buzdağı Yayınevi, 2020, Ankara | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 9 | 18.04.2024 | 1,5 | 50 | | End-of-term exam | 16 | 06.06.2024 | 1,5 | 50 | | |
| Student Work Load and its Distribution | | Type of work | Duration (hours pw) | No of weeks / Number of activity | Hours in total per term | | Yüz yüze eğitim | 4 | 14 | 56 | | Sınıf dışı çalışma | 2 | 14 | 28 | | Arasınav için hazırlık | 2 | 8 | 16 | | Arasınav | 1.5 | 1 | 1.5 | | Dönem sonu sınavı için hazırlık | 5 | 6 | 30 | | Dönem sonu sınavı | 1.5 | 1 | 1.5 | | Total work load | | | 133 |
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