Lifelong Learning Program

The objective of this talk is to give a brief intro on the topic of AI. Together with you, we will demystify AI and build a shared base-level understanding of the technology within your organisation. Inspiring industry examples will be given on the possibilities and limits of AI. Special attention will be given to the role of humans in creating the technology and products, and where they outperform computers. No technical background knowledge is needed to follow this lecture.

Difficulty level : Beginner
Duration : 40 minutes
Location : on premise/ online Indicative
Price* : 500€

This is a one to two-hour-long talk to do a deep dive on an area of AI. The specific topic and technical level of this talk will be customised to the needs of the participants from your organisation. They will also be agreed upon beforehand with you. Additionally, examples from your sector and a 20-minute Q&A are included in this talk.

Available topics for this talk:

• AI Demystified: What is AI? How does it work? What are opportunities and challenges? What are the trends?
• Legal Aspects of AI: Introduction to legal frameworks and issues.
• Introducing AI in my organisation: Is your organisation ready for AI? What skills do you need to acquire? What solutions are available in the market? How to scope and organise an AI project and manage risk?
• Technology walk-of-fame / AI tech drive: Overview of technologies, explained to non-technical audience, with success stories. We will introduce Deep learning, machine learning, Reinforcement Learning, Natural Language Processing (NLP), Recommender Systems, Logic, Search, Clustering, Bayesian Inference, Graph algorithms, Emergence & Multi-agent systems and Data mining at a conceptual level.
• AI applications: Overview of AI technology with examples and challenges in domain verticals. One can choose between: AI in education / AI in marketing / AI and creativity / AI in robotics / AI in cybersecurity.
• Human vs Machine: Where do machines outperform humans? What makes humans so unique? What are challenges of AI, and how can humans and machines complement each other?
• Show me the data: In this seminar we will dive deeper into the role of data. We will discuss how to define objectives, touch on data governance, management, operations, visualisation and representation.
• History & roots of AI: AI is an intrinsically interdisciplinary field. We will introduce its roots, discuss landmark papers, big ideas and philosophy behind AI.

Difficulty level: Beginner
Duration: 1 – 2 hours
Location: on-premise/ online/ AIXC
The seminars can be delivered at the AI Experience Centre (AIXC) on our campus for an extra €1000 euros. Two demonstrations of relevant AI lab projects are included in this case.

This training is an intensive 6-week program for groups of max. 12 people. It is centred around concrete use-cases in different industries (FinTech, marketing, hospitality, web & media, games, etc). A technical background is required for this training.
The learning outcome for participants will be one of the following :

Implement recommender systems, implement machine learning algorithms, perform time series prediction, build decision support-systems using reinforcement learning agents, implement multi-agent algorithms in concrete use-cases.

Difficulty level: For people with basic Python programming skills
Duration: 6 weeks
Location: AIXC

Tutorials are introductions to one specific technology in AI each time (see below). They are a combination of theory lectures and hands-on exercises. The capacity for this class is max. 5 people. A technical background is required for this training.

The following topics are currently available for tutorials:

• Planning, graphs & search algorithms: Despite its maturity, graph & state-space search algorithms remain a very powerful paradigm to approach problem-solving by computers. As such, it is still widely used, though not always visibly, in games, routeplanning, and for the optimization of operations. In this tutorial, we’ll cover uninformed search algorithms like depth-first and breadth-first, as well as informed search algorithms based on heuristics. Examples will be given to develop the learner’s ability to identify the best approach and design appropriate heuristics. We will also discuss related graph & planning algorithms.
• Natural Language Processing: In this tutorial, we will introduce the most important concepts and methods from Natural Language Processing and Information Retrieval. We will cover parsing, indexing techniques, semantic representations like vector spaces and word embeddings, query expansion and algorithms like classification, clustering and topic modelling. The above techniques will be applied and illustrated using examples from different domains such as document classification, search or chatbots.
• Data mining: In the early 90’s, during the second AI winter, many AI techniques were rebranded as data mining or knowledge discovery in databases (KDD). These methods, successfully applied in many businesses like retail and science, formed the basis of many data-driven evolutions that are now part of the “Machine Learning” hype. In this tutorial, we will cover the CRISP-DM data mining process, and several techniques, including clustering, anomaly detection, summarisation/visualisation, and association rule learning, and frequent itemset mining.
• Logic and constraint programming: Many tasks in Artificial Intelligence require reasoning over human-understandable symbols. This tutorial will introduce the main symbolic representation of knowledge, being logic, and explain how reasoning using formal logic functions. Also, the topic of constraint programming will be introduced, with a particular focus on the translation of business problems into formalised descriptions that can be solved using so-called advanced AI logic / constraint solvers.
• Recommender systems: Recommender systems are one of the big success stories of AI. In this session you will learn about content-based user/item based and utility-based systems. Matrix Factorization, context-aware systems, collaborative filtering, hybrid architectures and evaluation techniques will be explained in detail. Though they seem simple in theory, recommender systems are hard to implement in practice. To get a feel of the complexity, we will complement theory with exercises on realistic datasets

Difficulty level: For people with basic Python programming skills and a CS/EE degree.
Duration: 2 days
Location: AIXC

This offer is specifically for data scientists and AI Engineers working in R&D. For half a day (4 hours), they are allowed a backstage pass in the lab, where they will be able to ask a researcher from the lab all their questions about a specific solution. The researcher will assist them in going through the phases of hypothesis, experimentation and conclusion. If there is a skill gap, the researcher will give a speed training on the missing skill and provide reading material to follow-up (e.g. algorithmic programming).

Difficulty level: Professional
Duration: half day (4 hours)
Location: AI Lab

If you are in need of an expert opinion about Artificial Intelligence, we offer consulting services. This service is mostly targeted to executives and middle-management professionals.
The format is one-on-one teleconferencing calls, between an AI researcher from the lab and yourself. The question must be asked at least 3 days before, to allow the researcher to prepare a detailed and nuanced answer.

Difficulty level: /
Duration: /
Location: online
Indicative Price*: 450€/hour with preparation, 250€ if spontaneous.