Tensymp 2020


Speaker: Snehasis Banerjee, Scientist, TCS Research

Email: joysnehasis@gmail.com / snehasis.banerjee@tcs.com

Registration Link:



The discipline of Machine Learning has taken leaps and bounds in the recent years due to its large scale adoption in both academic and industry communities to tackle a large variety of complex problems pertaining to different domains. Although there exists large number of texts and online courses to offer required knowledge in machine learning, however they do not cover the thumb rules, the ‘art’ of model selection, the insights and tricks of the trade. This tutorial will introduce machine learning from a practitioner’s mindset. The attendees will be able to kick-start their choice of machine learning projects with the imparted knowledge and resources to build industry ready solutions quickly with meaningful steps. To provide a holistic view, fundamentals of different paradigms of machine learning will be explained lucidly. Hands-on session will help the attendees grasp the choice of machine learning approaches for specific problems, domains and data-set types.


Beginner to Intermediate.


Basics of computer programming knowledge.

Recommended prior knowledge:

Basics of statistics and python.

Table of Contents:

  1. Warm-up (necessary background and philosophy)
    1. Introduction
    2. Magic of ML - current applications
    3. Research Topics in ML
    4. Types of ML
      1. Supervised
        1. Classification
        2. Regression
      2. Unsupervised
      3. Reward based
  2. Paradigms of Machine Learning (coverage of different approaches)
    1. Analogy - fundamentals of Support Vector Machines
    2. Connection - fundamentals of Neural Network (covers Deep Learning)
    3. Evolution - fundamentals of Genetic Algorithms
    4. Probabilistic - fundamentals of Bayesian Algorithms
    5. Symbolic - fundamentals of Relational Learning
  3. Hands-on (live running of Python code)
    1. Sensor data analysis in Health-care and Machine Prognostics (raw sensor)
    2. Robot Visual Navigation (image)
    3. Ontology learning (text)
  4. Bonus (Jobs and Career Guidance)

Addendum: 10 intermediate quizzes and 10 question MCQ exit test at the end.

Speaker Bio:

Snehasis Banerjee, Senior Member, ACM, CSI & IEEE is a Scientist at TCS Research & Innovation, Kolkata. He did his Masters in Software Engineering from Jadavpur University. Currently he is working on TCS’s Cognitive Robotics project. He is leading the Global AI Channel of TCS Campus Commune. He serves as Secretary, ACM Kolkata Professional Chapter and recognized as AI Resource Person, CSI Region 2 (India East). He is Treasurer and Newsletter Editor of CSI Kolkata Chapter. He is CSI and India's representative to ISO Software & Systems Sectional Committee, driven by NASSCOM and nomination for upcoming ISO global standardization for AI. He is also the lead of CS Pathsala program (JV of TCS and ACM) to bring computational thinking to all schools in India, sponsored by Google for WB region. He has 18 publications at top rated journals and international conferences and have filed 18 patents in India and abroad, 8 of which have been granted so far. He has given several invited talks and tutorials at conferences in India and abroad and have been part of technical program committee and organizing committee of major events like ICML, IEEE SMC, IEEE Tensymp, IEEE BigData '19, IEEE IEEE Retis, IEEE ANTS, IEEE ISED, IEEE IPDPS, IEEE ICACCI, ACM Compute, ACM CODS COMAD. He has served as Jury in many contests such as IEEE RO-MAN, Digital Impact Square National Hackathon and ACM HackADay and Project Contests. He is part of the team that won the Tata Innovista 2014 Award for the IoT platform named TCS Connected Universe Platform. He was awarded best papers in Tactics Analytics Symposium and TCS Stylus Global Paper Contest. He has won the ACM-IEEE ICSE Prize 2019 and got registration award at NeurIPS 2019. He has also been an active volunteer for social empowerment in CSR activities of TCS and the Tata Group. His research interest in AI include Sensor Data Analysis, Cognitive Robotics, Reinforcement Learning & Semantic Reasoning.