Artificial Intelligence - AI1


AI-1 : ML/AI Basics

Statistics. Math. Computer Science. Physics. Long ago, the four disciplines lived together in harmony. Then, everything changed when the Computer Science attacked. Only a master of all four elements, could stop them, but when the world needed it most, it was not invented. A few years ago the world discovered the new master, a scientist called data scientist,a master of all four elements

Welcome to the First Edition of AI1. The objective of this module is to provide fundamental understandings of machine learning models and get you working with the basic concepts of ML and AI.

You will start with the regression models (KNN, Linear, Multi, Poly) and then move on to classification models (kNN, Logistic).

Finally, the course will provide a basic understanding of modern neural networks. Along the way, you will operationalize the key concepts of machine learning: picking the right complexity, preventing overfitting, regularization, and model evaluation.

At the end of this module, you will be able to run basic machine learning 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.

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

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 15 September 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: 8:00 PM - 9:30 PM - Saturday Series: 6:30 PM - 8:00 PM

Lab Sessions: - Thursday Series: 8:00 PM - 9:00 PM - Sunday Series: 6:30 PM - 7:30 PM

Office hours:

Please find a more detailed course schedule here.

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: No prior knowledge of machine learning libraries 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.

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:

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 courses.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.

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.

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