CS 559: Deep Learning

Fall 2021


Catalog Description: Overview of machine learning and its applications. Loss functions, numerical optimization and back-propagation. Fundamentals of feedforward neural networks. Modern architectures and techniques for training deep networks. Convolutional neural networks: basics, visualization, and techniques for efficient spatial localization in images. Recurrent neural networks and their variants. Applications of recurrent neural networks in language and image understanding, and image captioning. Recent advances in generative models learning, generative adversarial networks and variational auto encoders. Unsupervised and self-supervised representation learning. Deep reinforcement learning.

Recommended Books:

Assessment Methods:

Homework 20%
Literature Survey and Presentation 10% + %5
Midterm:Essay/written 30%
Project 35%

Any of the following will directly result in an F grade:

Passing Grade: No predetermined grade to pass the course.

Makeup Policy: Medical report holders will be entitled for the midterm make up. Makeup exam will be comprehensive.

Homework: TBD

Literature Survey and Presentation:


Template for the Reports:
All reports (including homework report) must be prepared using the IEEE double-column transactions article template (i.e. "bare_jrnl.tex").

Important Dates:

Event Date / Deadline
Midterm Exam TBD
Literature Survey Proposal submission
[subject line: cs559_2021f_survey]
8 October 2021, 23:59
Literature Survey Report submission
[via Moodle]
Literature Survey Presentation TBD
Homework submission (including the report)
[via Moodle]
Project Proposal submission
[subject line: cs559_2021f_proj_proposal]
11 October 2021, 23:59
Project Progress Presentation TBD
Project Progress Report submission
[via Moodle]
Project Final Presentation TBD
Project submission 
(including the report and presentation)
[via Moodle]

Tentative Schedule & Lecture Notes:
Lecture notes below are downloadable only within Bilkent network. Use VPN to access from home.

Week Topic Dates Lecture Notes
1 Introduction [online] 24 September 2021 (08:30-10:00)  
2 Basics [online],
Loss Functions [online]
29 September 2021 (11:00-12:50)
01 October 2021 (08:30-10:00)
3 Optimization 08 October 2021 (08:30-10:20)  
4 Feedforward networks and training 12 October 2021 (13:30-15:20)
15 October 2021 (08:30-10:20)
5 Convolutional neural networks 19 October 2021 (13:30-15:20)
22 October 2021 (08:30-10:20)

6 Spatial localization and detection

7 Segmentation
Understanding and Visualizing CNNs

8 Recurrent Neural networks

9 Word Embeddings and Language Models,

10 Project Progress Presentations

11 Unsupervised Learning and Generative Models
12 Deep reinforcement learning
13 Literature Survey Presentations

14 Project Presentations