Events Archive - Presciient

Loading Events

Upcoming Events

Events Search and Views Navigation

Event Views Navigation

July 2019

Fraud and anomaly detection: Sydney 29-30 July 2019

July 29 @ 9:30 am - July 30 @ 5:00 pm
Saxons Training Facilities, Sydney, Level 10, 10 Barrack Street
Sydney, NSW 2000 Australia
+ Google Map

This course presents statistical, computational and machine-learning techniques for predictive detection of fraud and security breaches. These methods are shown in the context of use cases for their application, and include the extraction of business rules and a framework for the interoperation of human, rule-based, predictive and outlier-detection methods. Methods presented include predictive tools that do not rely on explicit fraud labels, as well as a range of outlier-detection techniques including unsupervised learning methods, notably the powerful random-forest algorithm, which…

Find out more »

August 2019

Data transformation and analysis using Apache Spark: Melbourne, 12-13 August 2019

August 12 @ 9:30 am - August 13 @ 5:00 pm
Saxons Training Facilities, Melbourne, Saxons Training Facilities, Level 8, 500 Collins Street
Melbourne, Victoria 3000 Australia
+ Google Map

Course outline Developed by Jeffrey Aven, author of SAMS Teach Yourself Apache Spark and Data and Analytics with Spark using Python, this course will provide the core knowledge and skills needed to develop applications using Apache Spark. The “Data Transformation and Analysis Using Apache Spark” module is the first of three modules in the “Big Data Development Using Apache Spark” series, and lays the foundations for subsequent modules including “Stream and Event Processing using Apache Spark” and “Advanced Analytics using…

Find out more »

Advanced masterclass 2 – Random forests: Melbourne, 26-27 August 2019

August 26 @ 9:30 am - August 27 @ 5:00 pm
Saxons Training Facilities, Melbourne, Saxons Training Facilities, Level 8, 500 Collins Street
Melbourne, Victoria 3000 Australia
+ Google Map

This class will explore the many unique applications and extensions of the randomForest package, many of which are implemented in R. Access to these methods allows the user to easily solve problems not susceptible to other methods, including deep learning. Topics will include: A brief overview of the random forest algorithm. Out-of-sample estimates on training data, and applications in fraud, risk and outlier detection—random forests can make confident predictions on training data, unlike most other methods. Single-model quantile regression—estimating a…

Find out more »
+ Export Events