Bulletin and Active Deadlines

The Course

“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 prerequisite 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 be able to apply Deep Learning 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.

If you are only interested in the lectures, you can watch them on the YouTube channel.

Course description from student point of view

The course is well rounded in terms of concepts. It helps us understand the fundamentals of Deep Learning. The course starts off gradually with MLPs and it progresses into the more complicated concepts such as attention and sequence-to-sequence models. We get a complete hands on with PyTorch which is very important to implement Deep Learning models. As a student, you will learn the tools required for building Deep Learning models. The homeworks usually have 2 components which is Autolab and Kaggle. The Kaggle components allow us to explore multiple architectures and understand how to fine-tune and continuously improve models. The task for all the homeworks were similar and it was interesting to learn how the same task can be solved using multiple Deep Learning approaches. Overall, at the end of this course you will be confident enough to build and tune Deep Learning models.

Prerequisites

1. We will be using Numpy and PyTorch in this class, so you will need to be able to program in python3.
2. You will need familiarity with basic calculus (differentiation, chain rule), linear algebra and basic probability.

Units

Courses 11-785, 18-786, and 11-685 are equivalent 12-unit graduate courses, and have a final project. Course 11-485 is the undergraduate version worth 9 units, the only difference being that there is no final project.

Acknowledgments

Your Supporters

Instructors:

• Bhiksha Raj : bhiksha@cs.cmu.edu
• Rita Singh : rsingh@cs.cmu.edu

TAs:

• Aiswarya Vinod Kumar: avinodku@andrew.cmu.edu
• Akshat Gupta: akshatgu@andrew.cmu.edu
• Antioch Sanders: asanders@andrew.cmu.edu
• Anxiang Zhang: anxiangz@andrew.cmu.edu
• Bharat Gaind: bgaind@andrew.cmu.edu
• David Park: jinhyun1@andrew.cmu.edu
• Faris Alotibi: alotibifo@gmail.com
• Jacob Lee: jcob@cmu.edu
• Jiachen Lian: jlian2@andrew.cmu.edu
• Kushal Saharan: ksaharan@andrew.cmu.edu
• Mansi Anand: mansia@andrew.cmu.edu
• Miao Wang: miaow@andrew.cmu.edu
• Nicole Miya Sylvester: nsylvest@andrew.cmu.edu
• Reshmi Ghosh: reshmig@andrew.cmu.edu
• Sean Pereira: sapereir@andrew.cmu.edu
• Tony Qin: tcq@andrew.cmu.edu
• (Kigali) Abrham Gebresellasie: agebrese@andrew.cmu.edu
• (Kigali) Samuel Ishimwe: samuelmbaraga@gmail.com
• (Silicon Valley) Jingwei Zhang: jzhang5@andrew.cmu.edu
• (Silicon Valley) Joseph Konan: jkonan@andrew.cmu.edu
• (Silicon Valley) Yizhuo Zhang: yizhuoz@andrew.cmu.edu

Pittsburgh Schedule (Eastern Time)

Lecture: Monday and Wednesday, 8:00 a.m. - 9:20 a.m.

Recitation: Friday, 8.00am-9.20am

We will be using OHQueue and Zoom links listed on Piazza to manage office hours. The tentative schedule is as follows:

Course Work

Study groups

This semester we will be implementing study groups. It is highly recommended that you join a study group; see the forms on the bulletin.

Piazza: Discussion Board

Piazza is what we use for discussions. You should be automatically signed up if you're enrolled at the start of the semester. If not, please sign up. Also, please follow the Piazza Etiquette when you use the piazza.

AutoLab: Software Engineering

AutoLab is what we use to test your understand of low-level concepts, such as engineering your own libraries, implementing important algorithms, and developing optimization methods from scratch.

Kaggle: Data Science

Kaggle is where we test your understanding and ability to extend neural network architectures discussed in lecture. Similar to how AutoLab shows scores, Kaggle also shows scores, so don't feel intimidated -- we're here to help. We work on hot AI topics, like speech recognition, face recognition, and neural machine translation.

Media Services/YouTube: Lecture and Reciation Recordings

CMU students who are not in the live lectures should watch the uploaded lectures at Media Services in order to get attendance credit. Links to individual videos will be posted as they are uploaded.

YouTube is where non-CMU folks can view all lecture and recitation recordings. Videos marked “Old“ are not current, so please be aware of the video title.

Books and Other Resources

The course will not follow a specific book, but will draw from a number of sources. We list relevant books at the end of this page. 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.

You can also find a nice catalog of models that are current in the literature here. We expect that you will be in a position to interpret, if not fully understand many of the architectures on the wiki and the catalog by the end of the course.

Academic Integrity

Class Notes

Tentative Schedule of Lectures

Tentative Schedule of Recitations

Assignments