|
|
| BILL5190 | @Machine Learning | 3+0+0 | ECTS:7.5 | | Year / Semester | Fall Semester | | Level of Course | Second Cycle | | Status | Elective | | Department | DEPARTMENT of COMPUTER ENGINEERING | | Prerequisites and co-requisites | None | | Mode of Delivery | | | Contact Hours | 14 weeks - 3 hours of lectures per week | | Lecturer | Dr. Öğr. Üyesi Elif BAYKAL KABLAN | | Co-Lecturer | | | Language of instruction | | | Professional practise ( internship ) | None | | | | The aim of the course: | | The aim of this course is to provide a broad introduction to machine learning and statistical pattern recognition. It aims to teach topics such as supervised learning, unsupervised learning, learning theory, and reinforcement learning. The course allows you to explore recent applications in the field of machine learning and to design and develop algorithms for machines. |
| Programme Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | PO - 1 : | They will have a solid understanding of fundamental machine learning concepts and principles such as supervised and unsupervised learning, learning algorithms, model evaluation, and regularization. | 2,5,11 | 1, | | PO - 2 : | They will become familiar with various machine learning algorithms such as k-nearest neighbors, decision trees, Naive Bayes, artificial neural networks, and deep learning methods. They should understand how these algorithms work, their strengths and weaknesses, and when to use them in different scen | 2,5,11 | 1,5, | | PO - 3 : | They will gain practical experience in applying machine learning techniques to real-world problems. They will be able to preprocess data, select appropriate features, train models, tune hyperparameters, and evaluate the performance of machine learning models. | 2,5,8 | 1,5, | | PO - 4 : | They will develop practical application skills by working with popular machine learning libraries. They will be able to implement and deploy machine learning models using tools such as scikit-learn, TensorFlow, or PyTorch. | 2,5 | 1,5, | | 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), PO : Learning Outcome | | |
| Introduction to Machine Learning, Machine Learning Examples, k-Nearest Neighbors, Kernel Regression, Distance Metrics, Curse of Dimensionality, Linear Regression, Optimization, Generalization, Model Complexity, Regularization, Validation, Cross-Validation, Model Selection, Naive Bayes, Logistic Regression, Linear Discriminant Function, Perceptron, Multilayer Perceptron, Backpropagation, Softmax Classifier, Introduction to Deep Learning, Convolutional Neural Networks, Convolutional Neural Networks, Transfer Learning, Decision Trees, Ensemble Learning, Bagging, Random Forest |
| |
| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Course outline and logistics, An overview of Machine Learning | | | Week 2 | Machine Learning by Examples, Nearest Neighbor Classifier | | | Week 3 | Kernel Regression, Distance Metrics | | | Week 4 | Linear Regression, Optimization, Generalization, Model Complexity, Regularization | | | Week 5 | ML Methodology | | | Week 6 | Naive Bayes | | | Week 7 | Logistic Regression | | | Week 8 | Linear Discriminant Functions, Perceptron | | | Week 9 | Midterm | | | Week 10 | Multilayer Perceptron (MLP) | | | Week 11 | Backpropagation | | | Week 12 | Deep Learning | | | Week 13 | Convolutional Neural Networks | | | Week 14 | Decision Trees | | | Week 15 | Project Presentations | | | Week 16 | Final Exam | | | |
| 1 | A Course in Machine Learning, Hal Daumé III, 2017 | | | 2 | Russell and Norvig, Artificial Intelligence: A Modern Approach (3rd Edition). Prentice Hall, 2009
| | | 3 | Alpaydin, Introduction to Machine Learning (2nd Edition), MIT Press, 2010 | | | |
| 1 | Goodfellow, I, Bengio, Y, Courville, A., Deep Learning, MIT Press 2016 | | | 2 | Barber, Bayesian Reasoning and Machine Learning, Cambridge University Press, 2012
| | | 3 | Bishop, Pattern Recognition and Machine Learning, Springer, 2006 | | | 4 | Murphy, Machine Learning: A Probabilistic Perspective, MIT Press, 2012 | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | | | | | | | |
| 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 | 3 | 14 | 42 | | Sınıf dışı çalışma | 2 | 14 | 28 | | Arasınav için hazırlık | 2 | 5 | 10 | | Arasınav | 2 | 1 | 2 | | Proje | 1 | 14 | 14 | | Dönem sonu sınavı için hazırlık | 2 | 5 | 10 | | Dönem sonu sınavı | 2 | 1 | 2 | | Total work load | | | 108 |
|