Artificial Intelligence - AI2

AI-2 : Convolutional Neural Networks

Long ago in a distant land, I, Multi-Layer Perceptron, the shapeshifting master of darkness, unleashed an unspeakable evil. But, a foolish samurai warrior wielding a convolutional sword stepped forth to oppose me.Before the final blow was struck, I tore open a portal in time and flung him into the future where my evil is law. Now, the fool seeks to return to the past and undo the future that is MLP

Welcome to the First Edition of AI2, a follow-up to our introductory course AI1.

In this course, you will continue your data science journey to learn more about convolutional neural networks and how they’re being used for machine learning. We’ll emphasize both the basic algorithms and the practical tricks needed to get them to work well.

The objective of this module is to provide you, the student, with a deeper intuition and understanding of neural networks including network architecture choices, activation functions, feed-forward, convolutional neural networks and auto-encoders.

At the end of this course, you will be able to run a variety of advanced machine learning models, and learn to apply them to practical image recognition 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 program, 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.

The teaching assistants for the duration of this course are

Varshini Reddy

Hargun Oberoi

Anusha Sheth

Arya Mohan

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. - 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 Class

Welcome Session - Preparing for this class

There will be a Welcome Session scheduled on 03 November 2020 at 7:00 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: - Wednesday Series: 9:00 PM - 11:00 PM - Saturday Series: 7:30 PM - 9:30 PM

Lab Sessions:

Office hours:

Please find a more detailed course schedule here.

Course Pre-Requisites

You are expected to have programming experience at the level of Harvard’s CS50, statistics knowledge at the level of Harvard’s Stat 110 or above and basic machine learning concepts such as model fitting, test-validation, regularization, etc.

Please find a more detailed summary of the pre-requisites for this program here.

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:


[1] K. Stathoulopoulos and J. C. Mateos-Garcia, “Gender Diversity in AI Research,” SSRN Electronic Journal, 2019 [Online]. Available:

Logistics - What you need to begin?

We assume you have a Univ.AI account, created when you signed up at If not, email

Education software we use

All exercises and homeworks in this course will be done in jupyter notebooks. Detailed instructions to setup and work on Jupyter notebooks can be seen here.

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