Sunny day Sweepin’ the clouds away, On my way to where the air is sweet, Can you tell me how to get, How to get to Sesame Street?
Welcome to AI-3 A!
The objective of this module is to provide fundamental understandings of the latest language models built using deep learning architectures.
You will start with the traditional language models such as RNNs, LSTM & GRUs and later move on to intermediate topics like word embeddings, which lay the foundations of the modern deep learning State-of-the-art architectures for NLP.
At the end of this module, you will be able to build efficient language models, and tell how well they are performing.
This page introduces you to the team, the basic instructions, the schedule and various elements of our class.
Dr. Pavlos Protopapas
- Scientific Director of the Institute for Applied Computational Science (IACS).
- Teaches Introduction to Data Science (CS109a), Advanced Topics in Data Science (CS109b) and Advanced Practical Data Science (AC215).
- He is a leader in astrostatistics and he is excited about the new telescopes coming online in the next few years.
You can read more about him here.
Dr. Ignacio Becker
- Astronomer currently pursuing a Ph.D. in Computer Science at Pontificia Universidad Católica in Chile.
- His main area of research is applied AI to astrophysical problems.
- Nowadays, he focuses on developing models to process the real-time data of the next generation of telescopes.
Click on avatars of the TAs to know more about them.
We have very carefully designed the coursework to give you, the student, a wholesome learning experience.
Each week shall include:
- 2 Sessions
- 2 Labs
- Office hours
Session - What to expect
The course includes two 1.5 hour sessions per week. Before the session begins, students are expected to complete a pre-class reading assignment and and attempt a quiz based on the same.
A session will have the following pedagogy layout which will be repeated three times:
- Approx. 15 minutes of live online instruction followed by approx 15 minutes of Q/A + Quiz + Exercises.
- Sessions will help students develop the intuition for the core concepts, provide the necessary mathematical background, and provide guidance on technical details.
- Sessions will be accompanied by relevant examples to clarify key concepts and techniques.
After the session, students are expected to complete a short post-class quiz based on the principal concepts covered in class.
Lab - What to expect
A lab is a TA driven 1.5 hour session that is divided into 3 major parts.
Each lab begins by revisiting the quizzes and exercises done in the previous lecture session.
After discussing exercises, we will have a semi-formal Q/A session. All doubts pertaining, but not limited, to the previous session are welcome.
Your are expected to have a working knowledge of python, along with these three libraries:
All exercises in this course will be done in jupyter notebooks.
Note: Prior knowledge of high level machine learning libraries such as keras is necessary for this module
You are advised to follow the following two e-books for reference during the course:
Speech and Language Processing, Dan Jurafsky and James H. Martin
Natural Language Processing, Jacob Eisenstein
Before you begin the course, we have prepared for you a simple exercise to ensure your proficieny of the above libraries.
This will help you assess your preparedness for the course, and will also help you familiarize yourself with the platform.
Diversity & Inclusion
We actively seek and welcome people of diverse identities, from across the spectrum of disciplines and methods since Artificial Intelligence (AI) increasingly mediates our social, cultural, economic, and political interactions .
We believe in creating and maintaining an inclusive learning environment where all members feel safe, respected, and capable of producing their best work.
We commit to an experience for all participants that is free from – Harassment, bullying, and discrimination which includes but is not limited to:
- Offensive comments related to age, race, religion, creed, color, gender (including transgender/gender identity/gender expression), sexual orientation, medical condition, physical or intellectual disability, pregnancy, or medical conditions, national origin or ancestry.
- Intimidation, personal attacks, harassment, unnecessary disruption of talks during any of the learning activities.
 K. Stathoulopoulos and J. C. Mateos-Garcia, “Gender Diversity in AI Research,” SSRN Electronic Journal, 2019 [Online]. Available: http://dx.doi.org/10.2139/ssrn.3428240.