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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
About Me.
About Me.
Experience
Experience
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Posts
IIT Madras MS (Computer Science) Interview Experience
Published:
This year cut-off of MS program at IIT Madras, GATE score should be greater than 600 and UG percentage should be greater than 75%. They shortlisted 231 candidates for written test, which was held on 8th may 2018. Around 160 candidates were present out of 231 candidates. The written exam was started around 2 p.m on the same day. There were 2 papers 1) Objective paper 2) Subjective paper. 1) The objective paper contained 30 questions. Each question of 2 marks and negative marking was there -0.25 for each wrong question. A syllabus is same as GATE syllabus. Each subject contained 3 questions. The level of questions was good than GATE questions (More logical and need a little thinking). 5-7 questions were straightforward. Aptitude section had 5 questions and not easy at all like GATE aptitude. 2) The subjective paper had 2 questions. first one was about to build an algorithm to find all local minimum in given complete binary tree. A second was like a Statement of Purpose “Why do you want to pursue MS at IIT Madras”?
portfolio
Portfolio item number 1
Published:
Short description of portfolio item number 1
Portfolio item number 2
Published:
Short description of portfolio item number 2
publications
Correlated discrete data generation using adversarial training
Published in 2nd Workshop on Data Science for Human Capital Management collocated with ECML-PKDD, 2018
Deep Learning, Generative Adversarial Network, Autoencoder, Correlational Neural Networks.
Download here
A Service-Oriented Human Capital Management Recommendation Platform
Published in IEEE International System Conference (SysCon), 2019
Machine Learning, Recommendation, Human Capital Management.
Download here
Relational Boosted Bandits
Published in AAAI, 2021
Contextual Bandits, Relational Learning, Reinforcement Learning, Gradient Boosting
Download here
talks
Introduction to Machine Learning
Published:
- Lab sessions on various machine learning and data science algorithms and prepared tutorials in python for giving hands-on experience to attendees.
- I conducted theory sessions on basic ML algorithms like Stochastic gradient descent, Multivariate linear regression, Logstic regression and Kth nearest neighbour.
- More about summer school, you can find [Here]
Hands-on Machine Learning
Published:
- Lab sessions on various machine learning and data science algorithms and prepared tutorials in python for giving hands-on experience to attendees.
- I conducted theory sessions on ML paradigms and applied Logistic Regression and Decision Tree on real world datasets.
- Importance of data pre-processing and data wrangling to evaluation metric.
- You can find ipython notebook [Here]
Hands-on Machine Learning
Published:
- Lab sessions on various machine learning and data science algorithms and prepared tutorials in python for giving hands-on experience to attendees.
- I conducted theory sessions on ML paradigms and introduction to clustering.
- In-depth detail of K Means clustering algorithm with smart Initialization
- Implementing K means from scratch and visualization of step by step process in ipython notebook.
- You can find ipython notebook [Here]
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.