# PhD Interview Experience in Computer Science dept at IITs

The pic above I selected for header just to inform that CSE/AI interviews which I gave are mostly about maths only. Happy Reading

This blog is intended to help the aspirants who would like to pursue PhD in CSE/AI at Indian Institute of Technology(IITs) or IISc. I would be sharing with you my written test/interview experiences at following institutes:

• IISc Bangalore
• IIT Kanpur
• IIT Kharagpur
• IIT Delhi

All the above interviews/test of the above institutes I have given for the Autumn semester of 2021–22. Most of the IITs have B.Tech as an eligibility criteria and I was pursuing M.Tech at the time of interview with a CGPA of above 8 and got shortlisted for test/interview in all of the above institutes.

Broad topics covered under the interview & tests are Linear Algebra, Probability, Basics of ML & other CS subjects. I will list down the questions asked under the topics subheadings below:

Linear Algebra

i. Define vector subspace
ii. Whether invertible matrices are subspace of square matrices
iii. Define basis of vector space
iv. Describe a method to solve linear equations of 10–20 variables
v. How to calculate similarity between two vectors from different dimensional spaces

Probability

i. If two events are independent, whether their complements will be independent too
ii. if A & B are independent, P(A|B) = 0.3 , what will be P(A|B`)
iii. How to calculate covariance of two normal distributions

Algorithms

i. Find cycle in a directed graph
ii. Draw a circle with minimum radius which encompasses given points in 2-D space
iii. Check whether a given string is substring of other string

Machine Learning

i. What is Attention
ii. How is Embedding learned
iii. Why Naive Bayes Naive
iv. What if dependence between features is known, How to Calculate Bayesian Probability
v. How LSTM is better than RNN
vi. Explain the gates of LSTM and what are the alternatives of LSTM
vii. If there a language with no rules like ‘ I ate Apple’ , ‘ Apple at I’ both same which architecture will be used for training on such language
viii.Explain N-gram modelling and how to calculate bi-gram probability
ix. Can CNN be used on texts, why is it used on images ?
x. Visually explain an encoder decoder model
xi. Consider a NN with M units in input layer & N units in output layer with no hidden layer, what will be number of trainable parameters
xii. How to reduce variance of LSTM based model

Misc

i. Explain the process transition states in OS
ii. Difference between RISC & CISC
iii. Explain various addressing modes in Computer Architecture
iv. Draw graph of xy=c
v. Given any 4 points , which type of quadrilateral formed from those points

As most of these answers are easily available on Google, I am not including them in this post. If required may be in second part of it. I would like to summarise my experience of facing these questions in various interviews and some suggestions in following points :

• If you don’t know, say no
• When they ask for topic preferences, make sure to bring them to the topic you are very much comfortable with