Attend in-personat ADNEC Abu Dhabi
Attend onlinevia livestream
Date21-23 November 2021
Time09:00 to 17:00 GST/GMT+4
It is estimated that a typical organization loses about 5 percent of its revenues due to fraud each year. In this course, you will learn how machine learning can be used to fight frauds: you will understand when and how to apply supervised learning algorithms to detect fraudulent behavior similar to past ones, as well as unsupervised learning methods to discover novel types of fraudulent activities.
The course will not focus on the mathematics or theory, but on the practical applications: the course will provide a mix of technical and theoretical insights and shows you how to practically implement fraud detection models.
Moreover, during the course you will understand how to deal with the typical challenges of the fraud analytics task (e.g., data scarcity and imbalancing) and will get advice from real-life experience to help you prevent making common mistakes in the fraud detection domain.
This course is intended for students who have basic experience in the application of data science and machine learning to security-related tasks.
Furthermore, the course is also interesting for anyone who wants to understand how to effectively manage and detect frauds by exploiting data science and machine learning techniques.
– Frauds: Definition and Types
– Fraud Detection and Prevention
– Big data and Analytics
– Fraud analytics process model
– Develop a data-driven fraud-detection system
– Data Preprocessing Step
– Graphical and Statistical Outlier Detection
– Evaluating and Interpreting Clustering Solutions
– Linear Regression
– Logistic Regression
– Decision Trees
– Neural Networks
– Support Vector Machines
– Ensemble Methods: Random Forests
– Splitting Up the Data Set
– Performance Metric
– Demo and hands on a real fraud analytical engine
Michele received his Ph.D. degree cum laude in Information Technology from Politecnico di Milano in Italy, where he is currently a Contract Professor and a Postdoctoral Researcher working as part of the System Security group inside the Dipartimento di Elettronica, Informazione e Bioingegneria.
His research revolves around the application of machine learning methods to various cybersecurity-related fields, ranging from cyber-physical and automotive systems to binary analysis, going through fraud and intrusion detection. In particular, his research focuses mainly on the financial fraud detection task, where he worked on the analysis of advanced financial threats such as banking Trojans, on the design of frameworks to identify and timely detect fraudulent transactions, and on the security evaluation of detection systems against adversarial machine learning attacks.
He is actively involved in research projects funded by the European Union, and he is also co-founder of Banksealer, a Fintech spinoff of Politecnico di Milano.
This class is run a little different from most classes. We provide you purpose-built recorded lectures instead of trapping you in realtime with live-lectures. But fear not, the instructor is always right there eagerly waiting to mingle with the students and answer any questions you have. (The instructor really likes being asked questions. It shows you're paying attention ;)). One of many benefits is that you can watch lectures at 2x speed and zoom ahead of the other students and get to the hands on labs quicker. Or if there's bits of material you already know, you can just skip them and move on to the bits you don't know! Another big benefit is that you get to take the full lectures and labs with you! That means if you forget stuff and then need it in 6 months, you can quickly re-bootstrap yourself! Or you can watch the class twice, to really grow those neural connections and cement it in your brain! And unlike live lectures, our lectures are always getting more factually accurate, by having any accidental errors edited out.
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You can also opt to attend this class on 23 & 24 Nov instead. To do so, just email firstname.lastname@example.org