Learning outcomes
Upon successful completion of this course, you should be able to
- explain the foundations of natural language processing, computer vision, and generative artificial intelligence.
- apply deep learning techniques for natural language processing and computer vision applications.
- analyze transformers and attention mechanisms in natural language processing, computer vision, and generative artificial intelligence.
- explain the foundations of generative artificial intelligence and its various applications.
- ethically use deep learning models in natural language processing, computer vision, and generative artificial intelligence.
Evaluation
To receive credit for COMP 457, you must achieve a grade of at least D (50 percent) on each assignment and on the final examination.
The weighting of these grades is as follows:
| Activity | Weight |
| Assignment 1 | 15% |
| Assignment 2 | 20% |
| Assignment 3 | 20% |
| Assignment 4 | 15% |
| Final Exam | 30% |
| Total | 100% |
The final examination for this course must be requested in advance and written under the supervision of an AU-approved exam invigilator. Invigilators include either ProctorU or an approved in-person invigilation centre that can accommodate online exams. Students are responsible for payment of any invigilation fees. Information on exam request deadlines, invigilators, and other exam-related questions, can be found at the Exams and grades section of the Calendar.
Materials
This course either does not have a course package or the textbooks are open-source material and available to students at no cost. This course has a Course Administration and Technology Fee, but students are not charged the Course Materials Fee.
Links to the following course materials will be made available in the course:
Zhang, A., Lipton, Z. C., Li, M., & Smola, A. J. (2023). Dive into deep learning. GitHub. https://D2L.ai (OER)
Szeliski, R. (2021). Computer vision: Algorithms and applications (2nd ed.). https://szeliski.org/Book/ (OER)