“Deep Learning” systems, typified by deep neural networks, are increasingly taking over all AI tasks, ranging from language understanding, and speech and image recognition, to machine translation, planning, and even game playing and autonomous driving. As a result, expertise in deep learning is fast changing from an esoteric desirable to a mandatory prerequ isite in many advanced academic settings, and a large advantage in the industrial job market.
In this course we will learn about the basics of deep neural networks, and their applications to various AI tasks. By the end of the course, it is expected that students will have significant familiarity with the subject, and to be able to apply to them to a variety of tasks. They will also be positioned to understand much of the current literature on the topic and extend their knowledge through further study.
Instructor: Bhiksha Raj
TAs: Mohammed Ahmed Shah(email@example.com)
Time: Mondays, 1.30pm-2.50pm Doha time (12.30-1 .50 Kigali)
11-485/11-685 is open to all but is recommended for CS Seniors and Juniors, Quantitative Masters students, and non-SCS PhD students.
This course is an application elective of 6 units.
Grading will be based on homework assignments and a final project. There will be a minimum of two and a maximum of three assignments.
|Assignments||2 or 3, total contribution to grade 40%|
|Project||Total contribution to grade: 40%|
|Attendance||Mandatory, contribution to grade 20%|
Deep learning is a relatively new, fast developing topic, and there are no standard textbooks on the subject that cover the state-of-art, although there are several excellent tutorial books that one can refer to. The topics in this course are collected from a variety of sources, including recent papers. As a result, we do not specify a single standard textbook. However, we list a number of useful books at the end of this page, which we greatly encourage students to read, as they will provide much of the background for the course. We will also put up links to relevant reading material for each class. Students are expected to familiarize themselves with the material before the class. The readings will sometimes be arcane and difficult to understand; if so, do not worry, we will present simpler explanations in class.
We will use Piazza for discussions. Here is the link. Please sign up.
You are expected to comply with the University Policy on Academic Integrity and Plagiarism.
|Week||Start date||Topics||Lecture notes/Slides||Additional readings, if any|