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| TBB5224 | Deep learning | 2+2+0 | ECTS:7.5 | | Year / Semester | Fall Semester | | Level of Course | Second Cycle | | Status | Elective | | Department | DEPARTMENT of BIOSTATISTICS and MEDICAL INFORMATICS | | Prerequisites and co-requisites | None | | Mode of Delivery | Face to face | | Contact Hours | 14 weeks - 2 hours of lectures and 2 hours of practicals per week | | Lecturer | -- | | Co-Lecturer | | | Language of instruction | | | Professional practise ( internship ) | None | | | | The aim of the course: | | Deep learning, a branch of machine learning, allows computers to model high-level abstractions from experience (encoded in large-scale labeled and unlabeled data). Recent advances in computing hardware and algorithms have made it a popular tool for artificial intelligence. This course aims to provide the students with knowledge and application skills about the Deep Learning approach |
| Programme Outcomes | CTPO | TOA | | Upon successful completion of the course, the students will be able to : | | | | PO - 1 : | Basic knowledge of machine learning and deep learning methods. | | | | PO - 2 : | Knowledge and experience on how to apply deep learning methods in various domains such as computer vision, natural language processing and big data. | | | | PO - 3 : | Knowledge of literature with a focus on recent developments in deeep learning. | | | | 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 | | |
| Machine learning, deep learning methods, deep networks, alternating networks, optimization for training in deep models, repetitive networks and deep learning approache sample applications |
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| Course Syllabus | | Week | Subject | Related Notes / Files | | Week 1 | Introduction to Machine Learning | | | Week 2 | Machine Learning Basics | | | Week 3 | Deep Learning Tools | | | Week 4 | Feedforward Deep Networks | | | Week 5 | Regularization of Deep or Distributed Models | | | Week 6 | Optimization for Training Deep Models | | | Week 7 | Mid-term examination | | | Week 8 | Convolutional Networks | | | Week 9 | Sequence Modeling: Recurrent and Recursive Nets | | | Week 10 | Structured Probabilistic Models for Deep Learning | | | Week 11 | Linear Factor Models and Auto-Encoders | | | Week 12 | Application in Computer Vision | | | Week 13 | Application in Natural Language Processing | | | Week 14 | Application in Big Data | | | Week 15 | Application in Big Data | | | Week 16 | Final Exams | | | |
| 1 | Ian Goodfellow, Yoshua Bengio,2016,Deep Learning,MIT Press | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | In-term studies (second mid-term exam) | 6 | | 1 | 20 | | Quiz | 7 | | 1 | 30 | | Homework/Assignment/Term-paper | 15 | | 1 | 10 | | End-of-term exam | 16 | | 2 | 40 | | |
| 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 | 5 | 14 | 70 | | Arasınav için hazırlık | 2 | 6 | 12 | | Arasınav | 2 | 1 | 2 | | Ödev | 3 | 14 | 42 | | Dönem sonu sınavı için hazırlık | 2 | 16 | 32 | | Dönem sonu sınavı | 2 | 1 | 2 | | Total work load | | | 216 |
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