Data Science
Master Class

Innosential Labs and NASSCOM Present the First Ever Data Science Master
Class Delivered by Leading Lights in Data Science
from India and Across the Globe.

25th August-29th August 2017
What You will Learn at Master Class:
Choose From Over 30 Sessions
Enjoy the Global Exposure
Hackathons & Hands On Sessions

SPONSORS & PARTNERS

Event Speakers

Opportunity to connect with world renowned speakers, practitioners & researchers.

Prof. Jaime Carbonell
Director LTI, Carnegie Mellon University, USA
Mrs. Sushma Rajagopalan
ITC Infotech
Dr. Ganesh Mani
Adjunct Faculty, Carnegie Mellon University, USA
Dr. Sarabjot Singh Anand
Co-founder, Tatras Data, India
Mr. Atul Bansal
CEO, Timesys, USA
Mr. K.S. Viswanathan
Vice President (Industry Initiatives), NASSCOM
Mr. Ashok Reddy Bodanapu
Cyient Ltd.
Dr. Sandhya Chintala
NASSCOM
Prof. Susan Thomas
Indira Gandhi Institute of Development Research
Dr. Rajeev Rastogi
Director Amazon Machine Learning, Amazon
Mr. Vikram Gupta
Founder and Managing Partner, IvyCap Ventures Private Limited
Mr. Debasish Chatterjee
Global Data Officer, ITC Infotech
Dr. Shailesh Kumar
Distinguished Scientist (Vice President), OLA Cabs
Mr. Sanjay Jain
Chief Innovation Officer CIIE, IIM Ahmedabad
Prof. Bhiksha Raj
Carnegie Mellon University, USA
Dr. Arijit Laha
Senior Principal Data Scientist, Infosys
Prof. Marko Grobelnik
Josef Stefan Institute, Slovenia
Prof. C. A. Murthy
ISI kolkata, India
Dr. Vikas Agrawal
Senior Principal Data Scientist, Oracle
Prof. João Gama
Associate Professor, University of Porto, Portugal
Prof. N. Vishwanadham
Senior Scientist in Computer Science and Automation, IISc
Dr. Derick Jose
Co-founder, Flutura Decision Sciences & Analytics
Dr. Devesh Raj
Mercedes Benz
Dr. Venu Gopal Jarugumalli
GM, MRG, GHCL
Mrs. Madhumita Ghosh
Advance analytics, IBM India
Mr. Shridhar Srinivasan
IIM, Lucknow
Prof. Ashish Ghosh
ISI kolkata, India
Prof. Balaram Ravindran
Associate professor, IIT Chennai
Prof. Ajay Shah
Professor, National Institute of Public Finance and Policy
Dr. Vamshi Ambati
Founder, Predera, Ph.D, Carnegie Mellon University
Mr. Puneet Gupta
Chief Digital Officer, ITC Infotech
Mr. Sundara Ramalingam
Head Deep Learning Practice, Nvidia Graphics Private Limited
Mr. Mukundhan Srinivasan
Solution Architect Deep Learning, Nvidia
Dr. Manish Gupta
Director Machine Learning & Data Science, American Express
Mr. Kishore Gadiraju
Vice President Engineering, Solix Technologies
Mr. Srinivas Muktevi
Head of Data Analytics, Honeywell Technologies, India
Mr. Srikanth Gopalakrishnan
Vice President, IoT and Digital Connected Assets, SAP Labs India Private
Mr. Somshubhro (Som) Pal Choudhury
IoT Advisor, CIIE Bharat Innovation Fund

Event Schedule

The five day programme, to be held in Bangalore from the 25th August – 29th August, 2017 presents an opportunity for practitioner to immerse themselves in the world of data science.

What you will learn:

25th August

Give Your Feedback
08:AM
30

Opening

Sushma Rajagopalan, ITC Infotech
  Atul Bansal, Timesys
  Dr. Ganesh Mani, Carnegie Mellon University
  Dr. Sarabjot Singh Anand, Tatras Data
09:AM
00

Panel: The Art of Data Science: Real-World Illustrations

Moderator:Dr. Ganesh Mani, Adjunct Faculty, Carnegie Mellon University
Panelists: Mr. Vikram Gupta, IvyCap Ventures
  Mr. Debasish Chatterjee, ITC Infotech
  Dr. Shailesh Kumar, Ola Cabs
  Dr. Rajeev Rastogi, Amazon
  Prof. Susan Thomas, Indira Gandhi Institute of Development Research
10:AM
00

Learning from Data

Dr. Venu Gopal Jarugamalli and Dr. Sarabjot Singh Anand, Tatras Data

Topics covered include:

  • 1. Analytical Thinking, Warranty and Banking Use Cases, Types of Learning
  • 2. Instance and Hypothesis Spaces, Decision Boundaries, Bias Variance
  • 3. Learning as function approximation, Learning as Search, Probabilistic Models
  • 4. The impactof Outliers on learning –how to identify them?
  • 5. Multicollinearity, Principal Components Analysis and Factor Analysis
  • 6. Scaling and Normalization, Overfitting and Regularization
  • 7. Linear Regression, Lasso and Ridge Regression, Logistic Regression
  • 8. Model Evaluation: Cross Validation, Common Accuracy Measures, Confidence Interval, Confusion Matrices, ROC curves
12:PM
30

Lunch

01:PM
30

Machine Learning Algorithms

Prof. Ashish Ghosh, ISI Kolkata, with parallel hands-on demo by Dr. Sarabjot Singh Anand, Tatras Data

Nearest Neighbor, Decision Trees, SVM Ensembles – Bagging and Boosting

03:PM
00

Bayesian Graphical Models and Inference

Prof. C.A Murthy, ISI Kolkata, with parallel hands-on demo by Dr. Sarabjot Singh Anand, Tatras Data

Naïve Bayes, Bayesian Graphical Models, Structure Learning, Bayesian Inference

04:PM
30

Measuring Similarity

Dr. Arijit Laha, Infosys

The ability to recognize objects and their relationships is at the core of intelligent behavior. This, in turn, dependson one’s ability to perceive similarity or dissimilarity between objects, be physical or abstract ones. Hence, if we are interested to make computers behave with any degree of intelligence, we have to write programs that can work with a relevant representation of objects and means to compute their similarities or lack thereof, i.e. dissimilarity.

05:PM
15

Tea Break

05:PM
45

Hands-on Session

Dr. Sarabjot Singh Anand and Tatras Data team

Building Classification Models in the presence of class distribution skewness

Give Your Feedback

26th August

Give Your Feedback
08:AM
30

Opening

Atul Bansal, Timesys
  Dr. Ganesh Mani, Carnegie Mellon University
  Dr. Sarabjot Singh Anand, Tatras Data
09:AM
00

Hands-on Session

Dr. Sarabjot Singh Anand and Tatras Data team

Continued Building Classification Models in the presence of class distribution skewness

11:AM
30

Introduction to Neural Networks

Prof. Bhiksha Raj, Carnegie Mellon University

Perceptron Model, Neural Architectures, Back Propagation algorithm, Self-Organizing Maps

12:PM
30

Lunch

01:PM
30

Deep Learning Algorithms

Prof. Bhiksha Raj, Carnegie Mellon University

Convolution Neural Networks (CNN), Recurrent Neural Networks(RNN), Long short-term memory(LSTM)

04:PM
00

Application of Deep Learning in the Automotive and Manufacturing Industry

Dr. Devesh Raj, Mercedez Benz

Deep learning as applied to NLP and computer vision; generative adversarial networks (GANs) when labeled data is scarce

05:PM
00

Tea Break

05:PM
30

Creating a real-life AI Data Product Blueprint

Derick Jose, Flutura

We live in a VUCA world (Volatile, Uncertain, Complex and Ambiguous) world where the velocity of disruptions is dramatically increasing – Uber disrupting the taxi network, Amazon disrupting the neighborhood store, MOOCs disrupting the traditional educational model, how can AI world respond to these changes? This is a path breaking process which originated in Silicon Valley. It’s an actionable playbook for problem solvers. This is a hand on/application oriented practical session where at the end of 90 mins would have an AI product blueprint which you can then take back to your respective organizations for execution. This actionable AI product blue print can be a conversation starter and get your respective organizations mobilized to intercept the opportunities which present itself. This activity is multidimensional in nature. The blueprint which is the end deliverable would touch upon the following 5 areas: 1. The business dimension 2. The data dimension 3. The ML/AI/DL dimension 4. “Under the hood� architecture of AI data product 5. “Over the hood� architecture of AI data product

07:PM
00

Why Should You Believe My Model Predictions? Indeed, Why Should You?

Dr. Vikas Agrawal, Oracle

How do we reliably *explain* the classification or prediction done by a black-box ensemble of algorithms? How do we demonstrate to the end-user what the underlying relationships between the inputs and outputs are, for traditionally black-box systems? [This applies to explaining predictions from black box tools, & very partially to describing factors explaining a known outcome] How could we influence decision-makers enough to place trust in predictions made by a model?

Give Your Feedback

27th August

Give Your Feedback
08:AM
30

NVIDIA Hands-On Session: GPU Accelerated Deep Learning - The Path for Tomorrow

Sundara R. Nagalingam, NVIDIA

Deep learning is the fastest-growing field in Artificial intelligence, helping computers make sense of infinite amounts of data in the form of images, sound, and text. Preventing diseases, building smart cities, revolutionizing analytics - these are just a few things happening today with AI and specifically, deep learning. Today's deep learning solutions rely almost exclusively on GPU-accelerated computing to train and speed up challenging applications such as image, handwriting, and voice identification. This workshop will focus on how GPU-accelerated Deep Learning has evolved in the recent times with focus on application domains and tools.

09:AM
00

NVIDIA Hands-On Session

Mukundhan Srinivasan

Applications of deep learning with Caffe, Theano, and Torch; image classification with DIGITS

01:PM
00

Lunch

02:PM
00

Recommender Systems

Dr. Sarabjot Singh Anand, Tatras Data
  • - Content-based filtering, collaborative filtering, matrix-factorization-based approaches
  • - Contextual recommendation, conversational recommendation, group recommendation
  • - Recommendation diversity, evaluation of recommender systems; people, fashion and news recommendation
  • - Adaptive learning
04:PM
00

Time Series Analysis

Prof. BandiKamaiah, University of Hyderabad
05:PM
00

Tea Break

05:PM
30

Active, Proactive, Transfer and Multi-Task Learning

Prof. Jaime Carbonell, Carnegie Mellon University

Active and proactive learning; transfer and multitask learning, including their applications (case studies in finance, manufacturing, and customer service)

Give Your Feedback

28th August

Give Your Feedback
08:AM
30

Opening

Atul Bansal, Timesys
  Dr. Ganesh Mani, Carnegie Mellon University
  Dr. Sarabjot Singh Anand, Tatras Data
09:AM
00

Applications of Data Science to Manufacturing

Prof. N. Vishwanadham, IISc Bangalore

Decision making in supply chains is based on optimization models and the data from past sales. Several software packages have been developed and used in the Industry with the aim of delivering quality products to the customers at the right cost. Currently, there are several new trends that are happening in the supply chain arena. Globalization has created dispersed supply chains which are vulnerable and dependent on entities and factors that are exogenous to the supply chain. Also, technologies such as Big data, Cloud computing, Blogs, Social Media, Internet of Things and Mobility have become sources of large volumes and several varieties of data.
In this lecture, I would first present some recent big data start-ups that are revolutionizing or disrupting the traditional manufacturing networks. We then discuss how the new developments in tagging, sensing and embedding effects the four important supply chain processes: procurement, manufacturing, maintenance & repair and retail. Next, we present the big data ecosystem model: bigdata service chain, institutions (governments and social groups) and their influence on data availability, resources (natural, human, financial, and industry inputs) and delivery service infrastructure (communication and decision). This leads us to the question: what data should I collect, and what algorithms should I use to make decisions that would result in better business outcomes.
Data based decision making, particularly with unorganized and non-numerical data is a relatively unexplored area of research with abundant opportunities. The takeaways from this lecture are opportunities for both research and start-ups in this evolving area.

10:AM
00

Panel: Internet of Things 1, Things 2, and the AI Hat!

Moderator:Atul Bansal, Timesys
Panelists:Srinivas Muktevi, Honeywell Technologies
  Srikanth Gopalakrishnan, SAP Labs India Private Ltd
  Somshubhro (Som) Pal Choudhury, CIIE Bharat Innovation Fund
  Puneet Gupta, ITC Infotech
11:AM
00

Natural Language Processing

Prof. Jaime Carbonell, Carnegie Mellon University

Text categorization, sentiment categorization, named entity extraction, parsing, machine translation and dialogs (chatbots); classical constraint/rule systems, word embeddings, CRFs, and neural methods

01:PM
00

Lunch

02:PM
00

Real-Time Cross-Lingual Global Media Monitoring

Dr. Marko Grobelnik, Jozef Stefan Institute

Detailed explanation of the research and technology challenges implemented in a pipeline of modules in “Event Registry� (http://eventregistry.org/) for:

  • • collecting global news stories in real-time (250,000-500,000 articles per day from over 100,000 global sources) [topics like crawling the web data, cleaning the textual data, etc],
  • • linguistic and semantic enrichment of the collected information (using Wikifier.Org system, annotating a text into WikiData knowledge graph) [topics like language normalization, lexical, syntactical and light weight semantic annotation of texts including semantic disambiguation; classification, clustering, scalability issues, etc.],
  • • connecting linguistic and semantic information across languages (using XLing.ijs.si cross-lingual matching tool for 100 languages) [topics like large scale mapping between languages for information retrieval and classification],
  • • organization of enriched news stories into “eventsâ€? and “storylinesâ€? (i.e., related “eventâ€? sequences) [topics like how to make sense out of large amounts of textual data],
  • • providing individual and aggregated information through API and graphical user interface for further use [textual data visualization and semantic search].
  • Advanced topics include:
  • • Modeling news bias (how different news sources are reporting about the same events),
  • • Modeling causality in global society (how history is repeating itself),
  • • Probabilistic prediction of future events (how much can we tell about the future, knowing the past).
04:PM
00

Hands-On Session: Recommending News Articles

Dr. Sarabjot Singh Anand and Tatras Data team

The Task is to recommend news articles to a user given a set of articles previously read by the user. This hands on session will expose you to text analysis techniques such as tokenizing, stemming, lemmatization, named entity extraction, token weighting as well as semantic representations such as topic models.

05:PM
00

Tea Break

05:PM
30

Hands-On Session (Continued): Recommending News Articles

Dr. Sarabjot Singh Anand and Tatras Data team
Give Your Feedback

29th August

Give Your Feedback
08:AM
30

Opening

Dr. Sandhya Chintala, NASSCOM and Ashok Reddy Bodanapu, Cyient Ltd.
09:AM
00

A short Plenary panel about the importance of AI / ML / Analytics in the BFSI sector and some of your didactic experiences / learnings .

Moderator: Ganesh Mani, Carnegie Mellon University
Panelists: Dr. Ramesh Babu / Gopal Devanahali[MAGE]
       Prof. Jaime Carbonell, Carnegie Mellon University
       Prof. Ajay Shah , National Institute of public Finance and Policy

09:AM
30

Data Stream Mining I

Prof. Joao Gama, University of Porto
  • • Introduction to data streams
  • • Learning from data streams: classification and regression models, concept drift, clustering, frequent pattern mining
  • • Evolving networks: social network analysis, community detection, modeling evolution of communities
11:AM
00

Tea Break

11:AM
15

Data Stream Mining II

Prof. Joao Gama, University of Porto
  • • Introduction to data streams
  • • Learning from data streams: classification and regression models, concept drift, clustering, frequent pattern mining
  • • Evolving networks: social network analysis, community detection, modeling evolution of communities
12:AM
00

Panel discussion : 5th Age of Banking. How Analytics is transforming the BFSI industry

Moderator: Mr. Ramabhadran , Sr VP , Manipal Global Academy of Data Science
Panelists: Prof. Pulak Ghosh , IIM Bangalore
        Ganesh Mani , Carnegie Mellon University
        S G Venkatesh , Sr VP - BFSI , ITC Infotech
          Nidhi Prataoneni COO , Knowledge Services , EGS ,Wells Fargo
12:PM
45

Lunch Break

01:PM
30

Data Science for Digital Commerce

Dr. Manish Gupta, American Express

The talk focuses on the use of Data Science in digital commerce with applications in Recommendation based on cross selling and upselling, Dynamic Pricing and SEO optimization.

02:PM
15

Reinforcement Learning

Prof. Balaraman Ravindran, IIT Madras

Reinforcement learning is a paradigm that aims to model the trial-and-error learning process that is needed in many problem situations where explicit instructive signals are not available. It has roots in operations research, behavioral psychology, and AI. Recently Deep Reinforcement Learning methods have achieved significant successes by marrying the representation learning power of deep networks and the control lear ning abilities of RL. This has resulted in some of the most significant recent breakthroughs in AI such as the Atari game player and the Alpha Go engine from Deepmind. This success has opened up new lines of research and revived old ones in the RL community. In this talk, I will give introduce the reinforcement learning paradigm and briefly review the progress made in deep RL. We will discuss the problem of recommendations and that of Atari game playing in detail.

04:PM
30

Tea Break

04:PM
45

Building a World-Class AI/Data Science Team and Closing Remarks

Prof. Jaime Carbonell, Carnegie Mellon University and K.S. Viswanathan, NASSCOM

What skills sets are required in machine learning, analytics, and data bases? We will also discuss the importance of domain knowledge as well as leadership and support roles.

05:PM
45

Hands-On Session

· Exercise One: A simple bandit algorithm, corresponding to a recommendation application. The set up will be completely synthetic.

· Exercise Two: A simple spatial navigation problem. The domain will again be a simulated domain. This corresponds to an arcade learning environment, but the setting is simplified so that learning can be completed in the allotted time.

Give Your Feedback

BFSI

09:AM
40

Talk Using India Stack Adhar in BFSI

Sanjay Jain :
 Talk About The India stack and how that can provide the foundation for some BFSI innovation top of inserting data   using facile APIs .
10:AM
00

Hands On Session

Ms Subhashini Tripathi , Manipal Global Academy of Data Science Faculty

Venue

ITC WINDSOR, BENGALURU

#25, Windsor Square, Golf Course Road, Bengaluru 560052, India

More Information

Hotel Accommodation

ITC Windsor, Bengaluru is offering exclusive discounts for delegates of Data Science Masterclass

Delegates wishing to stay at ITC Windsor can email at info@ds-masterclass.com

Please mention in the email that you are “Delegates for Data Science Masterclass� along with the dates of your stay.

WHY ATTEND

Extraordinary Speakers

Discover advances in machine learning and artificial intelligence from the world's leading innovators. Learn from the industry experts in speech & image recognition, natural language processing and computer vision. Explore how AI will impact transport, manufacturing, healthcare, retail and more.

Discover Emerging Trends

The summit will showcase the opportunities of advancing trends in machine learning and their applications in business and society. Machine intelligence is enabling applications including virtual assistants, robots to help assisted living, and identifying items in videos. What else can it solve?

Expand Your Network

A unique opportunity to interact with industry leaders, influential technologists, data scientists & founders leading the machine intelligence revolution. Learn from & connect with 250+ industry innovators sharing best practices to advance machine learning and AI impact and opportunities.

Who Should Attend

Data Scientist, Al Engineer, Machine Learning Engineer, Data Analyst, Data Architect, CTO, CEO, Data Engineers, Student, Professor.

Join The Discussion

30 speakers, 250 leading technologists& innovators Group brainstorming sessions, Interactive workshops 7+ hours of networking access to all the filmed presentations. Discover technology shaping the future.

Downloads

View the summit brochure and all the information you need to convince your boss that attending the summit will help future-proof your business.

32 Sessions
60 Hours
18

+

Speakers

Join with us!

Participants will gain access to some of the best minds in the field, learn latest developments and implement these in evening, hands-on sessions. It will also give the data scientists opportunities to network with the visiting faculty and peers.

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1st August Rs. 50,000
  • 15+ Sessions
  • 18+ Professional Speakers
  • Networking Sessions
  • Food & Drinks
  • Evening Hackathons
  • Meet The Visiting Faculty
  • Pitch To The VCS
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