Course Objective

This course will enable you mastering machine-learning approaches in the area of investment management. It has been designed by two thought leaders in their field, Lionel Martellini from EDHEC-Risk Institute and John Mulvey from Princeton University. Starting from the basics, they will help you build practical skills to understand data science so you can make the best portfolio decisions.

The course will start with an introduction to the fundamentals of machine learning, followed by an in-depth discussion of the application of these techniques to portfolio management decisions, including the design of more robust factor models, the construction of portfolios with improved diversification benefits, and the implementation of more efficient risk management models.

Target Audience

Cybersecurity
Course
Program

1st Module

Introduction to
Cybersecurity concepts
(3 hours)

Eng. José Alegria (Altice)

Cyber Security definition
Importance of security at different layers (from physical to information)
Fundamental information security properties: confidentiality, integrity, availability
Types of vulnerabilities
Types of attacks
Motivations of attackers
Phases of an attack
Attack-Vulnerability-Intrusion (AVI) model
NIST Cybersecurity Framework: Identify, Protect, Detect, Respond, Recover

2nd Module

Identification of assets
and risk concepts
(3 hours)

Prof. Ana Respício (FCUL)

Resources
Information flows and dependencies
Security policies and responsibilities
Risk identification
Risk assessment
Risk analysis
Risk management
Control strategies
Cost-benefit analysis in InfoSec

1st Module

Introduction to
Cybersecurity concepts
(3 hours)

Eng. José Alegria (Altice)

Cyber Security definition
Importance of security at different layers (from physical to information)
Fundamental information security properties: confidentiality, integrity, availability
Types of vulnerabilities
Types of attacks
Motivations of attackers
Phases of an attack
Attack-Vulnerability-Intrusion (AVI) model
NIST Cybersecurity Framework: Identify, Protect, Detect, Respond, Recover

2nd Module

Protection of assets and detection of attacks
(3 hours)

Eng. Paulo Moniz (EDP)

Access control (Authentication, Authorization, Accounting)
Network protection (SSL/TLS, VPNs)
Infrastructure protection (Firewalls, IPS, Antivirus)
Information protection (backups, DLP tools)
Penetration testing
Personnel training
Intelligence gathering systems/OSINT
Vulnerability scanners
Event gathering and monitoring systems (Syslog, NIDS, HIDS)
Event correlation (SIEMs)

3rd Module

Protection of assets and detection of attacks
(3 hours)

Eng. Paulo Moniz (EDP)

Access control (Authentication, Authorization, Accounting)
Network protection (SSL/TLS, VPNs)
Infrastructure protection (Firewalls, IPS, Antivirus)
Information protection (backups, DLP tools)
Penetration testing
Personnel training
Intelligence gathering systems/OSINT
Vulnerability scanners
Event gathering and monitoring systems (Syslog, NIDS, HIDS)
Event correlation (SIEMs)

4th Module

Reaction & Recovery
(3 hours)

Eng. Marcelo Rodrigues (PWC)

Response plans (legal frameworks)
Incident management (CERT/CSIRT teams)
Analysis of incident impacts
Contingency plans
Disaster recovery
Business continuity
Image recovery and communication

5th Module

Cybersecurity Law
(3 hours)

Dra. Magda Coco (VdA)

The importance of the legal dimension
Main legal concepts
Cybersecurity legal and regulatory framework – main trends
The legal impacts of a cyber incident
A strategic approach to cybersecurity regulatory framework (general and sector-specific legislation)