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 AI4.

The objective of this module is to provide fundamental understanding of the concepts behind Reinforcement Learning, Generative Models 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. The course will also give an overview of network building blocks, followed by an review of Generative Adversarial Networks and their applications.

At the end of this module, you will be able to efficiently work with reinforcement learning problems and build effective generative adversarial networks.

This page introduces you to the team, the basic instructions, the schedule and various elements of our class.

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 (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. Achutha Kadambi

  • Assistant Professor at UCLA where he directs the Visual Machines Group.
  • Achuta received his PhD from MIT, his BS from UC Berkeley.
  • Achuta was named to the Forbes 30 under 30 list of leading inventors, and won a Google Faculty Award.

You can read more about him here.

Dr. Raghu Meka

  • Associate Professor of Computer Science at UCLA with a PhD from UT Austin.
  • He was a postdoctoral fellow at the Institute for Advanced Study, (at Princeton University).
  • Before joining UCLA Raghu was a researcher at the Microsoft Research.

You can read more about him here.

The teaching assistants for the duration of this course will be:

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.

Arya Mohan

  • Arya is working as a teaching fellow with Univ.ai, she also is currently a data analyst at Schneider Electric.
  • She is passionate about image processing and is currently researching applications of computer vision.

Kshitij Parwani

  • Kshitij is currently a student at IIT Varanasi and a Teaching Fellow at Univ.AI.

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 course consists of two parts:

Reinforcement Learning:

Generative Adversarial Networks:

The Class

Coming Soon!

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.

Our module policies around collaboration and grading are listed here. Our expectations of you are also laid out in that document.

Parting note

As you will learn in this course, data science is not just about writing efficient algorithms.

It requires proficiency in critical thinking, ideation & presentation, along with strong foundations in statistics, computer science & mathematics.

Keeping that in mind, you are adviced to give your full active attention to every session, homework & exercise.

We wish you best of luck for your data science journey.