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 group of students will present a paper related to the topics of the previous week.
Lectures: Mondays and Wednesday at 11:30-12:40 (Tower Second Floor)
Tutorials: Monday at 17:30-18:40 (SCI Z24)
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/COMP541.
COMP541 is open to all graduate students in our COMP department. Prospective senior undergraduate COMP students and non-COMP graduate students, however, should ask the course instructor for approval before the add/drop period. The prerequisites for this course are:
Grading for COMP541 will be based on
|Oct 3||Introduction to Deep Learning||Self-Assessment Quiz (Theory)|
|Oct 10||Machine Learning Overview||Self-Assessment Quiz (Programming)|
|Oct 17||Multi-Layer Perceptrons||Assg 1 out: MLPs and Backpropagation|
|Oct 24||Training Deep Neural Networks|
|Oct 31||Convolutional Neural Networks||Assg 1 due, Assg 2 out: CNNs|
|Nov 7||Understanding and Visualizing CNNs||Project proposal due|
|Nov 14||Winter Break|
|Nov 21||Recurrent Neural Networks||Assg 2 due, Assg 3 out: RNNs|
|Nov 28||Attention and Transformers|
|Dec 5||Graph Neural Networks||Assg 3 due, Assg 4 out: Transformers and GNNs|
|Dec 12||Autoencoders and Autoregressive Models||Project progress report due|
|Dec 19||Generative Adversarial Networks||Assg 4 due|
|Dec 26||Variational Autoencoders|
|Jan 2||Self-supervised Learning|
|Jan 9||Deep Neural Networks as Priors|
|Jan 16||Final Project Presentations|
|Jan 23||Final Project Presentations||Final project due|