ML2: Machine Learning and Data Science Part 2
Welcome to the First Edition of ML-2(also called DS-2). This is the second foundational course in data science. It follows DS 1 and builds on them. It develops your ability to use generative models and clustering. We introduce you to tree based models, ensemble models and boosting. The course will introduce you to bayesian models and culminates with decision theory and model interrogation, and how to deal with imbalanced data situations.After you finish this module, you will be ready to run complex data science analyses on all kinds of data on your own, dealing with data imbalance, and finally being able to make decisions with the models. 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.
Dr. Rahul Dave
Dr. Rahul Dave, former Data Science faculty at Harvard University, will be your instructor for Data Science 2. He was on the original team for Harvard’s famous Data Science course, cs109, and has taught machine learning, statistics, and AI courses, both at Harvard and at multiple conferences and workshops. You can read more about him here. In addition to the classes, you can schedule problem-solving classes with the faculty and mentors throughout the rest of the week.
The teaching assistants for the duration of this course are:
- Anusha is working as a Research and Teaching fellow with Univ.ai, she has previously worked with Robert Bosch as a software engineer in the automotive domain.
- She is a big believer in sustainability and has been working on applications of AI in sustainable energy for the last 3 years.
- Arya is currently a data analyst at Schneider Electric and a Teaching Assistant at Univ.AI.
- She is passionate about image processing and is currently researching the application of computer vision in the detection of deep fake videos.
We have very carefully designed the coursework to give you, the student, a wholesome learning experience.Each week shall include:
- 2 Sessions
- 1 Lab
- 2 Office hours
Before the session begins, students are expected to complete a pre-class reading assignment 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 and optional post-class reading will be provided.
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 homework are welcome.
- The last part of the labs deals with upcoming homework or new exercises. It is directed towards giving a brief overview of the homework problem. We will discuss some code to help you get started.
The Welcome Session
There will be a Welcome Session scheduled on 8th March 2021 at 9:00 PM IST for all registered students. Please check your mail for more information.
NOTE: Below timings are in IST
- Lecture Sessions: -
- Tuesday Series: 9:00 PM - 11:00 PM
- Friday Series: 9:00 PM - 11:00 PM
- Lab Sessions: - Saturday Series: 7:30 PM - 8:30 PM
- Office hours: -
- Mondays: 9:00 PM - 10:00 PM
- Wednesdays: 9:00 PM - 10:00 PM
This is a part of ML-X Course, hence you should have completed ML-1 before this
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 .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.
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 firstname.lastname@example.org. 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.
- We will be providing homeworks and labs on Github, hence you should have a Github account.Occasionally, we may conduct in-class contests on kaggle. Please register on Kaggle and familiarize 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 set up a jupyter lab and get you acquainted with jupyter notebooks.Our module policies around collaboration and grading are listed here. Our expectations of you are also laid out in that document.