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Content of the programme

Generative Artificial Intelligence

Course name: Generative Artificial Intelligence

Period: International Business Consultancy (Y4:13/14)

Cluster within the International Business Consultancy Programme: Business Skills and Languages

Study load: 2 ECTS (~56 hours)

Course description

Generative Artificial Intelligence provides an in-depth understanding of generative artificial intelligence and its applications. It covers foundational principles, advanced techniques, ethical considerations, and practical implementation strategies for generative AI projects. The course also delves into emerging trends and real-world applications of generative AI across various industries, offering a comprehensive view of the field.

Competences and learning objectives

Competences: information management, designing and changing.

Learning objectives

  • Gain a solid understanding of generative AI principles and transformer models;
  • Explore core principles of generative models and their underlying mathematical foundations;
  • Develop skills in prompt engineering for effective interaction with generative AI models;
  • Understand various architectural designs and frameworks for building generative AI solutions;
  • Delve into retrieval-augmented generation architectures for enhanced AI performance;
  • Address ethical implications and challenges in the development and deployment of generative AI;

General course information

Required previous knowledge

Basic statistics and/or programing knowledge of is considered useful, but not mandatory.

1. AI Made Simple: A Beginner’s Guide to Generative Intelligence by Rajeev Kapur (2023). This book is listed as one of the best Generative AI books and is recommended for beginners who are looking to understand Generative Intelligence.
2. Generative Deep Learning, 2nd Edition by O'Reilly Media. A practical book that teaches machine learning engineers and data scientists how to use TensorFlow and Keras to create generative deep learning models from scratch, including variational autoencoders (VAEs), generative adversarial networks (GANs), Transformers, normalizing flows, energy-based models, and denoising diffusion models.
3. Introduction to Generative AI by Manning Publications. This book introduces cutting-edge AI tools and the practical techniques needed to use them safely and effectively. It emphasizes on Generative AI tools like ChatGPT and provides an online subscription option for access.
4. Artificial Intelligence: A Guide to Intelligent Systems. This book provides an excellent overview of AI, covering topics like expert systems, machine learning, and neural networks, which are essential to mastering generative AI.
5. Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville. A comprehensive book covering various aspects of deep learning, including generative models.
6. Attention is All You Need by Ashish Vaswani, et al. This paper introduced the Transformer model, which is fundamental to understanding modern generative AI models.
7. Auto-Encoding Variational Bayes by Diederik P. Kingma and Max Welling. This paper introduced Variational Autoencoders (VAEs), a key concept in generative models.

And also weekly prescribed articles, readings, videos, tutorials and/or datasets.

Way of working

During classes, participants will engage in hands-on analysis and project work to understand and apply generative AI principles. It's expected that students come to class prepared, having reviewed the assigned readings and ready to participate in discussions. Weekly discussions on trends, innovation, and the impact of data analysis and generative AI will be held, fostering a collaborative learning environment. Through practical assignments and project work, students will have the opportunity to apply the concepts learned in a real-world context, further solidifying their understanding and skills in generative AI.

Exam

Final Project where a Generative AI solution will be created.

Caesura

A final score of 5,5 or higher leads to passing the course and receiving the related 2 ECTS.

Retake exam

The retake of the exam is scheduled at the end of the semester. This is usually two or three weeks after the first exam week.