MSA 8650 (Advanced Deep Learning)
Fall 2023 Updated on Aug 14, 2023 Section 6 (CRN: 93243): Mondays 6:00 pm - 8:30 pm
Email Office Hours Link Saber Soleymani (instructor) ssoleymani@gsu.edu Mondays 2:00 - 3:00 pm Webex Meetings Sai Roopesh Mandava (Teaching Assistant) smandava4@student.gsu.edu Thursday 2:00 - 3:00 pm
Course Description
Advanced Deep Learning is designed to provide students with an in-depth understanding of neural networks and the latest techniques and applications in deep learning. The course covers topics such as computer vision, natural language processing, generative models, deployment, and more. Students will have the opportunity to work on real-world projects and gain hands-on experience with the latest libraries and tools in deep learning.
Learning Objectives
By the end of the course, students should be able to:
- Understand the foundations of neural networks and deep learning.
- Apply deep learning techniques to computer vision and natural language processing tasks.
- Deploy neural network-based applications and create APIs.
- Work with generative models to create new data samples.
- Utilize graph neural networks and recommender systems.
- Aware of the latest libraries and applications in the field of deep learning.
Textbooks
- Goodfellow, Ian, et al. Deep Learning. MIT Press, 2016.
- Chollet, François. Deep Learning with Python. Manning Publications, 2017. Available here
Note: This course does not require the purchase of a textbook. However, the above textbooks are recommended for further reading and reference.
Weekly Schedule
| Date | Week number | Location | Agenda |
|---|---|---|---|
| 08/21 | Week 1 | Online | Course Introduction, A brief recap of Deep Learning |
| 08/28 | Week 2 | In-person | Foundations of Neural Networks |
| 09/11 | Week 3 | In-person | Computer Vision (part I) |
| 09/18 | Week 4 | In-person | Computer Vision (part II) |
| 09/25 | Week 5 | Online | Computer Vision (part III) |
| 10/02 | Week 6 | In-person | Natural Language Processing (part I) |
| 10/09 | Week 7 | Online | Natural Language Processing (part II) |
| 10/16 | Week 8 | Online | Natural Language Processing (part III) |
| 10/23 | Week 9 | In-person | Neural Networks Deployment |
| 10/30 | Week 10 | In-person | Generative Models (part I) |
| 11/06 | Week 11 | Online | Generative Models (part II) |
| 11/13 | Week 12 | In-person | Graph Neural Networks |
| 11/27 | Week 13 | In-person | Recommender Systems |
| 12/04 | Week 14 | Online | Presentations & Latest Libraries and Applications |
| Assignment | Due Date |
|---|---|
| Reading 1 | Due [09/10] (before week 3) |
| Quiz 1 | From [10/02] (week 6) |
| Assignment 1 | Due [10/08] (before week 7) |
| Reading 2 | Due [10/22] (before week 9) |
| Assignment 2 | Due [11/26] (before week 10) |
| Final Group Project | On [12/04] (week 14) |
| Bonus Assignment | Due [12/06] |
The weekly schedule and deadlines are tentative and subject to change. Please check the updates on iCollege.
Grading Policy
Letter Grade Breakdown
| A+ (97-100+) | B+ (87–89.4) | C+ (77–79.4) | D (59–69.4) |
| A (91–96.9) | B (83–86.9) | C (72–76.9) | F (0–59.9) |
| A- (89.5–90.9) | B- (79.5 –82.9) | C- (69.5 –71.9) |
Points Breakdown
| Points | Assignment |
|---|---|
| 10 | Reading 1 |
| 10 | Reading 2 |
| 10 | Quiz 1 |
| 10 | Assignment 1 |
| 25 | Assignment 2 |
| 25 | Final Group Project |
| 10 | Participation & Discussions |
| 5 | Bonus Assignment |
Campus Safety
Georgia State University values the safety of all university community members on all of our campuses. To promote campus safety, the university is providing the LiveSafe app free for all students, faculty, and staff. This app provides a quick, convenient, and discrete way to communicate with the GSU police. I strongly recommend that you download the app from either the Apple App Store or Google Play. You can sign-up for Panther Alerts and learn more about LiveSafe by visiting the GSU LiveSafe webpage: https://safety.gsu.edu/livesafe/.
In addition, please make sure you have the campus police numbers in your phone.
- For emergencies call 404-413-3333
- For non-emergencies and to request a safety escort call 404-413-2100
- If you are hearing impaired call 404-413-3203
Accommodations for Students with Disabilities
Students who wish to request accommodation for a disability may do so by registering with the Access and Accommodation Center. Students may only be accommodated upon issuance by the Access and Accommodation Center of a signed Accommodation Plan and are responsible for providing a copy of that plan to instructors of all classes in which accommodations are sought. Please note that accommodations are not retroactive and that students with accommodation plans should provide those to instructors at the beginning of each term.
Course Policies
Attendance
Students are expected to attend all in-person and online classes to gain the full benefit of the course. However, missing one class without explanation is accepted for this course. If you are unable to attend a class, you need to study the materials of the class you missed and engage more in discussions in future classes. If you miss more than one class, you should explain the reason for your absence in the form at https://forms.gle/QrWT62pXTiFqqt8A8
Make-up Quizzes
The lowest quiz grade will be dropped. If you miss a quiz, that will be considered as your lowest grade. In case of an emergency, you should explain the reason in the form at https://forms.gle/QrWT62pXTiFqqt8A8 by selecting the “missing a class” option.
Late/Missed Work
Assignments that are turned in late are subject to a late penalty. 20% deduction will be applied to the submission which is submitted late for up to 48 hours after the deadline. After 48 hours, you will automatically get Zero. If you have a valid reason for missing a deadline, you should explain the reason in the form at https://forms.gle/QrWT62pXTiFqqt8A8
Email Policy
As instructors can see more information about students and their enrolled sections, it is preferable to use iCollege’s messaging system rather than emails. For questions regarding grades, please email the TA and CC the instructor.
Collaboration Policy
All work is expected to be your own for each exam, quiz, homework, and project. No collaboration is allowed unless otherwise stated in the instructions.
Academic Honesty
We must abide by GSU’s Academic Honesty Policy.
Generative AI Policy
You are allowed to use generative AI tools to help with assignments for this course but only with the following conditions:
Attribution is necessary. You must identify the parts of your submissions (assignments, projects, etc.) that you generated using AI tools.
You should fully understand the generated content (including programming code) and be able to explain it to the instructor if asked.
Failure to comply with these conditions will be considered a violation of the Academic Honesty Policy.
Laptop & Technology Statement
You will need a computer to do the assignments. We will be using e-devices in different capacities in the class. Please make sure to silence your e-devices during the meetings.
Online Class Etiquette
- Treat each online session as if you were present in class.
- Mute your microphone when it is not in use.
- Identifying the speaker is sometimes difficult, especially when the screen is shared. Let others know your name whenever you start speaking.
Syllabus Policy
The course syllabus provides a general plan for the course; deviations may be necessary. The syllabus may be updated throughout the course, so you should check iCollege to ensure you are reading the latest version. The instructor will send an announcement to the class if there has been a major change in the syllabus.
Your constructive assessment of this course plays an indispensable role in shaping education at Georgia State. Upon completing the course, please take the time to fill out the online course evaluation.