This course provides a thorough understanding of the fundamental concepts and recent advances in deep learning. The main objective is to provide students practical and theoretical foundations to use and develop deep neural architectures to solve challenging tasks in an end-to-end manner. The course is taught by Aykut Erdem.
The course will use Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville as the textbook (draft available online and for free here).
Instruction style: During the semester, students are responsible for studying and keeping up with the course material outside of class time. These may involve reading particular book chapters, papers or blogs and watching some video lectures. After the first three lectures, each week a student will present a paper related to the topics of the week.
Lectures: Wednesday at 13:30-16:30 (Seminar Room)
The course webpage will be updated regularly throughout the semester with lecture notes, presentations, assignments and important deadlines. All other course related communications will be carried out through Piazza. Please enroll it by following the link https://piazza.com/hacettepe.edu.tr/spring2020/cmp784.
CMP784 is open to all graduate students in our CENG department. Prospective senior undergraduate students may sit in on the class. Non-CENG graduate students, however, should ask the course instructor for approval before the add/drop period. The prerequisites for this course are:
Grading for CMP784 will be based on
|Feb 26||Introduction to Deep Learning|
|Mar 4||Machine Learning Overview|
|Mar 11||Multi-Layer Perceptrons||Practical 1 out|
|Mar 18||Classes suspended due to COVID-19 outbreak|
|Mar 25||Classes suspended due to COVID-19 outbreak||Practical 1 due|
|Apr 1||Training Deep Neural Networks|
|Apr 8||Convolutional Neural Networks||Practical 2 out|
|Apr 15||Understanding and Visualizing CNNs|
|Apr 22||Recurrent Neural Networks||Project proposal due|
|Apr 29||Attention and Memory||Practical 2 due|
|May 6||Midterm Exam (guide)|
|May 13||Autoencoders and Autoregressive Models|
|May 20||Generative Adversarial Networks and Flow Models||Project progress report due|
|May 27||Variational Autoencoders|
|Jun 3||Self-supervised Learning|
|Jun 10||Final Project Presentations|