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Blog Post number 4

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

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Blog Post number 2

less than 1 minute read

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

teaching

TA : Calculus (MA105)

Undergraduate course, IIT Bombay, 2016

I was a TA for a course title Calculus at IIT Bombay. The course is meant for freshmen students. This course deals with proofs of all the calculus students use offhandedly in their JEE preparation, and later covers some multivariable calculus. In general, many students struggle with this course. The course instructor was Prof A.K. Pani.

TA : Computer Programming and Utilization (CS101)

Undergraduate course, IIT Bombay, 2019

I was a TA for CS101, an introductory course in Programming at IIT Bombay. The course is meant for freshmen students. The course uses C/C++ to introduce students to the beautiful world of programming. The course starts slow, but has a steep learning curve. This is especially true whenever we get to the topic of recursion. Most of the course is just C programming, with STL introduced at the very end. The course instructor was Prof. Ganesh Ramakrishnan.

TA : Probabilistic Learning and Decision Making (CSE150A)

Undergraduate course, UC San Diego, 2020

I was a TA for CSE150A, an course in AI at UC San Diego. This is an upper division course, which was being taught in the summer session. The course mainly deals with looking at Machine Learning as a whole from a probabilistic angle. The course starts with basics of Probability Theory, then covers inference and learning in Bayesian Networks in that order. The course finally ends with a brief introduction to Reinforcement Learning. The course instructor was Joseph Geumlek.