| School/Faculty/Institute |
Graduate School |
| Course Code |
CSE 603 |
| Course Title in English |
Advanced Machine Learning |
| Course Title in Turkish |
İleri Yapay Öğrenme Teknikleri |
| Language of Instruction |
EN |
| Type of Course |
Flipped Classroom |
| Level of Course |
Advanced |
| Semester |
Fall |
| Contact Hours per Week |
| Lecture: 3 |
Recitation: 0 |
Lab: 0 |
Other: 0 |
|
| Estimated Student Workload |
187 hours per semester |
| Number of Credits |
7.5 ECTS |
| Grading Mode |
Standard Letter Grade
|
| Pre-requisites |
None |
| Expected Prior Knowledge |
None |
| Co-requisites |
None |
| Registration Restrictions |
Only Doctorate Students |
| Overall Educational Objective |
To learn advanced machine learning methods and how to design and implement intelligent systems to make predictions such as classification and regression. |
| Course Description |
This course aims to teach advanced machine learning concepts. Topics neural networks, deep learning, hyperparameter tuning, regularization, optimization of machine learning models, and convolutional neural networks. Students will learn how to apply advanced machine learning techniques to complex real-world problems and analyze the performance of these systems. |
| Course Description in Turkish |
Bu dersin amacı ileri yapay öğrenme tekniklerini öğretmektir. Ders içeriği ileri yapay sinir ağları, derin öğrenme yöntemleri, parametre seçimi, düzenlileştirme, makine öğrenmede kullanılan optimizasyon teknikleri ve evrişimli sinir ağlardır. Öğrenciler, bu derste ileri yapay öğrenme yöntemlerinin güncel gerçek hayat problemlerine nasıl uygulanacağı öğrenip, başarım analizlerini yapabileceklerdir. |
Course Learning Outcomes and Competences
Upon successful completion of the course, the learner is expected to be able to:
1) Apply advanced classification methods to recognize patterns;
2) Apply advanced regression methods to estimate unknown functions;
3) Assess the performance of advanced machine learning methods;
4) Design and construct an advanced machine learning system for a given problem
|
| Program Learning Outcomes/Course Learning Outcomes |
1 |
2 |
3 |
4 |
Relation to Program Outcomes and Competences
| N None |
S Supportive |
H Highly Related |
| |
|
|
| |
Program Outcomes and Competences |
Level |
Assessed by |
| Prepared by and Date |
, |
| Course Coordinator |
TUNA ÇAKAR |
| Semester |
Fall |
| Name of Instructor |
Asst. Prof. Dr. TUNA ÇAKAR |
Course Contents
| Week |
Subject |
| 1) |
Introduction to Neural Networks |
| 2) |
Shallow Neural Networks I |
| 3) |
Shallow Neural Networks II |
| 4) |
Deep Neural Network I |
| 5) |
Deep Neural Network II |
| 6) |
Regularization Methods |
| 7) |
Optimization Methods |
| 8) |
Hyperparameter Tuning, Batch Normalization |
| 9) |
Transfer Learning |
| 10) |
Convolutional Neural Networks I |
| 11) |
Convolutional Neural Networks II |
| 12) |
Applications of Deep Neural Networks I |
| 13) |
Applications of Deep Neural Networks II |
| 14) |
Applications of Deep Neural Networks III |
| 15) |
Final Examination Period |
| 16) |
Final Examination Period |
Information Package / Course Catalogue