On 23 January 2018 from 14:00 to 17:00 (Computer Lab, ground floor, Department of Business Studies-Roma Tre University, Via Silvio D’Amico, 77 -00145, Roma), Dr. Francesca Perino, Application Engineer at MathWorks, will hold a min-workshop on “Machine Learning and Big Data Analytics with MATLAB”.
The attendance of the course is free, but for organizational reasons it is necessary to register to register on the following web page:
At the heart of many financial applications are machine learning techniques used for risk classification, economic analysis, credit scoring, time series forecasting, estimating default probabilities, and data mining. Big data represents an opportunity for quantitative analysts and data scientists alike to impact the way organizations make informed business decisions. By building machine learning models that harness big data, a greater level of insight and confidence can be achieved.
However, implementing and comparing machine learning techniques to choose the best method can be challenging. Furthermore, there is no single approach to solving the many challenges arising from working with big data. MATLAB minimizes these challenges by providing you with a number of built-in functions and tools for quick prototyping, integration, and scaling, to take you from initial prototype all the way to business-critical production system.
In this session, we will introduce ways of working with big data systems, the different types of machine learning techniques in MATLAB, how to determine the best techniques for your problem by evaluating model performance, and rapidly deploying your machine learning models into production. We will cover several new workflows and data types in MATLAB and the toolboxes that have been designed to address the most common challenges with big data analytics and machine learning.
Data management and integration with databases, live market data, and big data environments
Efficient workflows for heterogenous time-series data using new data management capabilities
Parallel Computing techniques to speed up long-running computations and deal with out-of-memory data
Predictive modeling and using supervised machine learning techniques to build a credit rating engine