Speaker: Sri Krishnamurthy

Chief Data Scientist and CEO of www.quantuniversity.com

Sri Krishnamurthy, CFA, is the founder of QuantUniversity, a data and quantitative analysis company, and the creator of the Analytics Certificate program and the Fintech Certificate program. He has more than 15 years of experience in analytics, quantitative analysis, statistical modeling, and designing large-scale applications. Previously, Mr. Krishnamurthy has worked for Citigroup, Endeca, and MathWorks and has consulted with more than 25 customers in the financial services and energy industries. He has trained more than 1,000 students in quantitative methods, analytics, and big data in the industry and at Babson College, Northeastern University, and Hult International Business School, many of whom work in data science roles at financial services firms. Mr. Krishnamurthy earned an MS in computer systems engineering and an MS in computer science from Northeastern University and an MBA with a focus on investments from Babson College.

Find Sri Krishnamurthy at

https://twitter.com/quantuniversity
https://www.linkedin.com/in/srikrishnamurthy/

Workshop : Anomaly Detection Workshop for Developers

Location : Cyril Magnin I
Day of week : Monday
Duration : 9:00am - 4:00pm

Anomaly detection (or Outlier analysis) is the identification of items, events or observations which do not conform to an expected pattern or other items in a dataset. It is used is applications such as intrusion detection, fraud detection, fault detection and monitoring processes in various domains including energy, healthcare and finance.

In this workshop, we will discuss the core techniques in anomaly detection and discuss advances in Deep Learning in this field.

Through case studies, we will discuss how anomaly detection techniques could be applied to various business problems. We will also demonstrate examples using R, Python, Keras and Tensorflow applications to help reinforce concepts in anomaly detection and best practices in analyzing and reviewing results.

What you will learn:

  • Anomaly Detection: An introduction

  • Graphical and Exploratory analysis techniques

  • Statistical techniques in Anomaly Detection

  • Machine learning methods for Outlier analysis

  • Evaluating performance in Anomaly detection techniques

  • Detecting anomalies in time series data

  • Case study 1: Anomalies in Freddie Mac mortgage data

  • Case study 2: Auto-encoder based Anomaly Detection for Credit risk with Keras and Tensorflow

See sample here:  https://www.slideshare.net/QuantUniversity/anomaly-detection-core-techniques-and-advances-in-big-data-and-deep-learning

Session page

2019 Tracks

  • Groking Timeseries & Sequential Data

    Techniques, practices, and approaches around time series and sequential data. Expect topics including image recognition, NLP/NLU, preprocess, & crunching of related algorithms.

  • Deep Learning in Practice

    Deep learning use cases around edge computing, deep learning for search, explainability, fairness, and perception.

Schedule