AI-4 A : Reinforcement Learning
Look for the bare necessities,
The simple bare necessities,
Forget about your worries and your strife
I mean the bare necessities,
Old Mother Nature’s recipes,
That brings the bare necessities of life!
Welcome to the First Edition of AI4 A. The objective of this module is to provide fundamental understanding of the concepts behind Reinforcement Learning and how to apply them to real world problems. This course follows the Univ.AI model of balancing between concept, theory, and implementation.
The course covers an introduction to the field of Reinforcement Learning covering the basic concepts, dynamic programming, Q-learning and Policy Graident Methods.
At the end of this module, you will be able to efficiently work with reinforcement learning problems.
This page introduces you to the team, the basic instructions, the schedule and various elements of our class.
Interested in joining?
If you would like to apply to this course, please go here.
We also provide this course as part of our Masters and Accelerated program, check this link out to get more information and apply.
The Team
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 (AC295).
- 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.
Javiera Astudilo
- Currently enrolled in the Data Science MS at GSAS, Harvard University.
- She’s been working as a research assistant and TA at IACS, Harvard University and Pontificia Universidad Católica de Chile.
- Previously, she worked as a Data Scientist for the retail industry.
Varshini Reddy
- Varshini is an Artificial Intelligence Researcher and Teaching Fellow at Univ.AI.
- Previously, she was a Research Associate at the Indian Institute of Science.
- She is passionate about working at the interface of AI and social impact.
The Coursework
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
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 one 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, and homeworks are welcome.
The last part of the labs deals predominantly with the upcoming homeworks. It is directed towards giving a brief overview of the homework problem. We will discuss some code to help you get started.
The Class
Welcome Session - Preparing for this class
There will be a Welcome Session scheduled on Monday, 7th June 2021 at 7:30 PM IST for all registered students. Please check your mail for more information.
High level course schedule
NOTE: Below timings are in IST
Lecture Sessions:
Tuesday Series:
7:30 PM - 9:00 PM IST [ 10:00 AM - 11:30 AM EST ]Saturday Series:
6:30 PM - 8:00 PM IST [ 9:00 AM - 10:30 AM EST ]
Lab Sessions:
Wednesday Series:
7:30 PM - 9:00 PM IST [ 10:00 AM - 11:30 AM ]Sunday Series:
6:30 PM - 8:00 PM IST [ 9:00 AM - 10:30 AM EST ]
Office hours:
- Mondays:
7:30 PM - 8:30 PM [ 10:00 AM - 11:00 AM EST ]
Course Pre-Requisites
Your are expected to have a working knowledge of python, along with these three libraries:
- Numpy
- Pandas
- Tensorflow.keras
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
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 [1].
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.
Reference:
[1] 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.
Logistics - What you need to begin?
We assume you have a Univ.AI account, created when you signed up at course.univ.ai. If not, email programs@univ.ai.
Education software we use
- Our lectures and labs are carried out via Zoom (install instructions).
- Quizzes & exercises will be conducted on the digital learning platform Ed.
- Ocassionally, we may conduct in-class contests on kaggle. Please register on kaggle and familarize yourself with it, if you haven’t already done so. This is a short video that will help you learn how to use kaggle.
All exercises and homeworks in this course will be done in jupyter notebooks. This link will help you setup jupyter lab and get you acquianted with jupyter notebooks. Please contact univ.ai for more information.