Artificial Intelligence - AI4


AI-4 : Generative Models and 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. The objective of this module is to provide fundamental understanding of the concepts behind Generative Models and Reinforcement Learning and apply them to real world problems. This course follows the Univ.AI model of balancing between concept, theory, and implementation.

The course is split into 2 parts. It starts with generative models which covers autoencoders, variational autoencoders and Generative Adversarial Networks. The second half of this module, will introduce you to the field of Reinforcement Learning. Additionally, the course will begin with an overview lecture that is perhaps a review of various encoder decoder frameworks, with some practical applications in computer vision.

At the end of this module, you will be able to build efficient generative models, and 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

You can read more about him here.

Dr. Achutha Kadambi

You can read more about him here.

Dr. Raghu Meka

You can read more about him here.

The Coursework

We have very carefully designed the coursework to give you, the student, a wholesome learning experience.

Each week shall include:

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:

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.

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:

Lab Sessions:

Office hours:

Course Pre-Requisites

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

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:

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

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.

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