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: Thursday 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/spring2018/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 15||Introduction to Deep Learning|
|Feb 22||Machine Learning Overview|
|Mar 1||Multi-Layer Perceptrons||Practical 1 out|
|Mar 8||Training Deep Neural Networks|
|Mar 15||Convolutional Neural Networks||Practical 1 due, Practical 2 out|
|Mar 22||Understanding and Visualizing CNNs||Project proposal due|
|Mar 29||Recurrent Neural Networks|
|Apr 5||Attention and Memory||Practical 2 due, Practical 3 out|
|Apr 12||Midterm Exam (guide)|
|Apr 19||Representation Learning|
|Apr 26||Deep Generative Models Part I||Practical 3 due|
|May 3||Deep Generative Models Part II||Project progress report due|
|May 10||Deep Reinforcement Learning|
|May 17||Final Project Presentations|