CEng 501 - Deep Learning - Fall 2025

Images generated by the “Stable Diffusion” method. From left to right: “a bunch of students learning deep neural networks” by Picasso, Van Gogh and Da Vinci.

Instructor: Emre Akbas; e-mail: emre at ceng dot metu.edu.tr; Office: A-403; Office hours: click + by apppointment
Lectures: to be announced
Online communication: (forum, homework submissions) https://odtuclass.metu.edu.tr/
Syllabus: click here

Announcements

Sep 5, 2025 - I am receiving too many e-mails about taking this course as a special student, as an undergrad, from other departments/universities, etc. I am not able to respond each of them. If you want to take the course, please make sure you attend the first lecture. And, please see my answers to frequently asked questions.

Late submission policy

Any work, e.g. an assignment solution, that is submitted late past its deadline will receive -10 points per day delayed. For example, if the deadline is Oct 14, 23:59 and a student submits his/her work anytime on Oct 15, that work will be evaluated over 90 instead 100.

Detailed syllabus

An important note: Lecture slides provided below are by no means a complete resource for studying. I frequently use the board in class to supplement the material in the slides.

Week 1

Lecture slides and resources will appear here on a weekly basis.

Frequently Asked Questions about taking the course

I am receiving too many e-mails about taking the course. Unfortunately, I cannot reply them one by one. Below are answers to common questions.

Q1: Can I take the course?

A1: There is a huge demand for the course from all kinds of backgrounds. Thanks. However, I have the responsibility to evaluate your learning outcomes and grade you. Therefore, I need to limit the number of seats. Based on my previous years’ experience, this limit will be around 35-40.

Since this is a graduate METU CENG course, I need to give priority to the graduate students in our department. Here is the priority order that I will use to accept students to the class. From high to low priority:

Graduate students from METU CENG,
A limited number of 4th year undergraduate students from METU CENG,
Graduate students from other METU departments,
Special students (see http://oidb.metu.edu.tr/ozel-ogrenci, you need to be a grad student in some other university to be eligible).

I must note that the first two categories (METU CENG students) almost fill up the whole capacity. So, unfortunately, there might not be much room for the remaining two categories.

Also, precedence will be given to students who are actively doing research in machine learning and related areas. This course is not a PyTorch or Keras tutorial, we intend to go beyond the “user” level.

You might want to check out the other three DL courses offered in METU: MMI727, EE543, and CENG403.

Machine learning background is required. If you have not taken a machine learning course before, please do not take this course.

Fluency in Python is required.

This course assumes that the student has taken already a course on the fundamentals of deep learning and is familiar with conventional models such as Multi-Layer Perceptrons, Convolutional Neural Networks, Recurrent Neural Networks and Long-Short Term Memory Networks.

Q2: How can I register for the course?

A2: Come to the first lecture. In the first lecture, I will collect information from the participants and then, will decide (based on my answer A1 above) on who will be able to register. This enrollment list will be announced in a couple of hours following the first lecture. Students listed in this enrollment list will be able to add the course during the add-drop period.

Q3: I was able to take the course during the regular interactive registration. Should I worry about not being accepted?

A3: No worries. You will stay.

Q4: Can I take this course as a special student?

A4: Possible but unlikely. Please see my answer A1 above.

Q5: Even if I don’t officially register for the class, can I audit it?

A5: Yes, definitely.