Anthill Inside Miniconf – Pune
Machine Learning, Deep Learning and Artificial Intelligence: concepts, applications and tools.
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Harshad Saykhedkar Getting started with machine learning: tools, algorithms and conceptsThis workshop will serve as a starting point for beginners in machine learning. I will cover a high level overview of field of machine learning and introduction to the Python data ecosystem in machine learning. I strongly believe that the best way to learn machine learning is by building few algorithms from scratch. So we will build a supervised ML application from scratch in Python. Since ML is … more
Section: Workshop
Technical level: Beginner
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Harshad Saykhedkar Bayesian methods in data analysis, an introductionIf you are in a sector where the outcome of your data analysis and machine learning work has significant monetory impact, then you should learn bayesian data analysis! more
Section: Full talk
Technical level: Beginner
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Vishal Fundamental Math Concepts for Data Science / ML / AIMany beginners intrigued by Data Science/ML/AI behold it in the awe and fear reserved for a hairy monster, A lot of really interested, good prorammers seem to maintain distance from it because they are just plain scared of the math. The workshop will be a refresher of the basic concepts and does not assume any prior knowledge greater than addition, subtraction, multiplication and division. more
Section: Workshop
Technical level: Beginner
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Vishal (Not so) Straight (!) fun with Linear RegressionWe’ll conduct a live experiment with the help of volunteers and then analyze the data collected using linear regression. more
Section: Full talk
Technical level: Beginner
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saurabh agarwal  Inference in Deep Neural NetworksA lot of focus is currently on training neural networks and better architecture. But we don’t focus alot on inference because well we are busy making our models work. Inference is supposed to run millions of time more than training and alot of times the inference is supposed to run on embeded devices. This talk will go into details of how the advancements in hardware have made Deep Learning possi… more
Section: Full talk
Technical level: Intermediate
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SM
Sameer Mahajan Leapfrog in Deep LearningMachine learning (ML) gives computers the ability to learn without being explicitly programmed. Evolved from the study of pattern recognition and computational learning theory in artificial intelligence, ML explores the study and construction of algorithms that can learn from and make predictions on data through building a model from sample inputs. It’s a really exciting & impactful phase in the … more
Section: Workshop
Technical level: Intermediate
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SD
Shubham Dokania Deep Reinforcement Learning: A hands-on approachDeep Reinforcement Learning has been becoming very popular since the dawn of DeepMind’s AlphaGo and DQN. Algorithms that learn to solve a game (sometimes better than) humans seems very complex from a distance, and we shall unravel the mathematical workings of such models through simple processes. This workshops aims to provide a simple insight about Reinforcement Learning and going to Deep RL. more
Section: Workshop
Technical level: Intermediate
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Leelavati Narlikar Machine Learning in Molecular BiologyWhy do we need new machine learning algorithms to solve problems in molecular biology? Most “plug and play” packages cannot be applied directly, because often it is not even clear how to pose the problem as one of machine learning. Also, high-throughput biotechnologies keep evolving, producing different “types” of data, so the methods have to keep up. I will show how probabilistic models based on… more
Section: Full talk
Technical level: Beginner
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Ajay Kelkar Analytics without paralysis!Storytelling with Data is becoming much more common today because of both vast amounts of data being available in the public space & also the emergence of a newer breed of younger, more “social” professionals who consume such data with far more ease! AI & machine learning are also changing the context within which you can tel data stories.In this talk I will look at examples of how data insights … more
Section: Crisp Talk
Technical level: Beginner
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Satish Gopalani Applying ML in AdTech and Lifecycle of an ML projectDescription: This talk will provides insights into how ML is being applied to solve real world problems in AdTech and at scale in PubMatic. It will also cover the entire lifecycle of a typical Machine Learning Project. more
Section: Full talk
Technical level: Beginner
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SR
Shourya Roy How similar are two pieces of text? A moderately broad and deep dive in one of the fundamental topics in NLP.I will talk about a fundamental problem of measuring similarity between two pieces of text. This problem appears in many contexts from search and information retrieval, natural language inferencing, plagiarism detection, answer scoring, machine translation, (near) duplicate detection etc. I will give an overview of some fundamentals, key formulations and approaches of work that is present in the … more
Section: Full talk
Technical level: Intermediate
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SR
Shourya Roy How similar are two pieces of text? A moderately broad and deep dive in one of the fundamental topics in NLP.I will talk about a fundamental problem of measuring similarity between two pieces of text. This problem appears in many contexts from search and information retrieval, natural language inferencing, plagiarism detection, answer scoring, machine translation, (near) duplicate detection etc. I will give an overview of some fundamentals, key formulations and approaches of work that is present in the … more
Section: Full talk
Technical level: Intermediate
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SR
Shourya Roy How similar are two pieces of text? A moderately broad and deep dive in one of the fundamental topics in NLP.I will talk about a fundamental problem of measuring similarity between two pieces of text. This problem appears in many contexts from search and information retrieval, natural language inferencing, plagiarism detection, answer scoring, machine translation, (near) duplicate detection etc. I will give an overview of some fundamentals, key formulations and approaches of work that is present in the … more
Section: Full talk
Technical level: Intermediate
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SD
Swapnil Dubey Doing Data Science on CloudWith the increase in data size for running DS models,it is important to look into possible infrastructure options which provide enough scalability to run DS algo successfully.Optimal use of infrastructure in terms of cost is the need of hour.For example,running task using multiple GPU for finite amount of time. Almost all the Cloud vendors(AWS,Google,Microsoft) provide different kind of services … more
Section: Full talk
Technical level: Intermediate
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SD
Swapnil Dubey Image Classification using Support Vector Machines.In machine learning, support vector machines are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis.In this talk we will be looking at the basic fundamentals and implementation of SVM for image classification. more
Section: Full talk
Technical level: Beginner
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JS
Jayesh Sidhwani Build intelligent, real-time applications using Machine LearningThe surge in the availability of large datasets, processing powers and the ability to process the data in real-time has opened up a plethora of opportunities in which Machine Learning algorithms can harness this power to build intelligent, real-time applications. more
Section: Full talk
Technical level: Intermediate
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Aditya Prasad Narisetty Applied Machine Learning for realtime #FairPlay against FraudFor any firm processing online transactions, ensuring a strong shield against fraud is of top priority. And for platforms hosting fantasy sports and online gaming, ensuring a fair play from all users and real-time fraud detection is a first line of defence. Traditionally, rule based engines formed the crux of anomaly and fraud detection. But maintaining a rule engine and adapting to new patterns … more
Section: Flash talks
Technical level: Intermediate
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