Artificial intelligence and Cyber security learning modules

In this module, you will learn about the basics of artificial intelligence (AI) and cyber security (CS). AI and machine learning are changing the way we live and how we work. Within this short course, you will recognize the user needs and find the intersection between user needs and AI strengths, collect and evaluate the required data. This CS course provides a strong foundation on the fundamentals of cybersecurity, basic security concepts and web-based risks mitigation.
The piloting of the modules for AI and CS have been prepared as a project week where learners are going to work in smaller groups. On the first day, experts from companies will present and explain the areas of artificial intelligence and cybersecurity. The learners will then be grouped and learn about the challenges they are going to work on during the week. The second and fourth days are dedicated to solving challenges, finding a solution and preparing a presentation of the activities carried out. On day three, a virtual tour of the company Kolektor Digital is planned. The last day is reserved for finalizing the presentations and presenting the learner’s ideas/solutions to company experts and teachers. The learners will be awarded micro-credentials for gained skills.
Cyber security: Defines and describes principles of information security, identifies and Explains most common vulnerabilities in modern web servers, explains web application security flaws (OWASP). Artificial Intelligence: Describes the fields where AI is used (computer vision, speech recognition, natural language processing, social network filtering, games, mobile advertising,…), finds the examples where AI is probably better and where a rule or heuristic-based solution will work, defines user needs, possible solutions and assess whether AI can uniquely solve the problem.
CS • Lists basic security elements • Identify features of common web server architecture. • Identify web server and application vulnerabilities. • Describe web server and web application attacks. AI • identifies which users problems is AI positioned to solve; • asses automation vs. augmentation; • queries data from multiple sources like relational databases, IoT devices, social networks, data warehouses
CS: basic security concepts, web-based risks mitigation; AI: recognize user needs and find the intersection between user needs and AI strengths, collection and evaluation of the required data

Learning from the best

Miloš Pešić
Milos Pešić is an advisory board member and award-winning Global Head of Information & Cyber Security (SC Awards 2021) utilising over 15 years of global experience from strategic, operational, advisory and technical leadership roles across finance, telecom and healthcare sectors. A professional leader with a track record of successful delivery of security transformation for enterprise-size companies. At his current position at “Marken a UPS company”, he has an external SOC team of analysts and internal Cybersecurity, Data Scientist and GRC teams. Milos is a regular guest speaker at cybersecurity conferences and holds MSc – Information Security from Royal Holloway and two BA from Information Security and Computer Sciences.

Learning from the best

Mateja Lavrič
Mateja Lavrič is a managing director at Kolektor Ventures, a corporate venture capital fund, investing in startups that work with exponential technologies and are developing solutions for industrial environment. Also member of Kolektor Digital’s core team. Kolektor Digital is an emerging business unit, established by multinational Kolektor, based in Idrija, Slovenia, with 37 manufacturing companies around the globe and more than 5.500 employees. Digital develops solutions for smart factories.

Learning from the best

Borut Sluban
Borut Sluban is leading a team of data scientists in the Business Intelligence Department at GEN-I. His team focuses on supporting the company’s commodities trading, risk management, and innovative energy services by developing analytical and data-driven solutions. Prior to joining GEN-I he was working as a scientific researcher at the University of Zurich in Switzerland and at the Jožef Stefan Institute in Slovenia. His work interests are in the broader area of data science, where he enjoys combining analytics, machine learning and data visualization for predictive modeling and discovering insights from data.

More similar courses

It seems we can't find what you're looking for.