Generative Artificial Inteligênce

Recommended Prerequisites

Advanced Machine Learning, Autonomous Intelligent Agents, Python programming; Good English reading, writing and speaking skills; Mathematics and statistics.

Teaching Methods

During the lectures (T) the concepts, the theories, the algorithms will be presented and discussed. In the lab classes (PL) students will consolidate what was learned in T. The practical assignments will be done under the supervision of the teacher. Grading will be based on two components: (1) projects involving the techniques; (2) a research work.

Learning Outcomes

The UC aims to study and develop generative AI models using connectionist, evolutionary, and biological approaches. Based on previous knowledge acquired throughout the course, the main challenges and opportunities in this field will be analyzed. Emphasis will be given to hands-on learning, following a Project Based Learning approach.

By the end of the unit, the student will have a comprehensive understanding of the generative AI field and will also be capable of developing and/or adapting generative systems to address real-world needs, applying these approaches in various domains.

 

The main competencies to be developed are:

Instrumental – analysis and synthesis, problem-solving

Personal – critical thinking

Systemic - practical application of the theoretical knowledge; research

 

The secondary competencies are:

Instrumental – organizing and planning

Personal – work in teams

Systemic – autonomous learning; creativity

Work Placement(s)

No

Syllabus

Introduction to Generative AI

            Overview and applications

            Distinction between generative and discriminative models

            Historical context and evolution

 

Classical Generative AI Methods

            Rule-based and expert systems

            Production systems

            Constraint logic programming

 

Evolutionary Generative Models

            Evolutionary approaches

            Representations

            Fitness

            Interaction

 

Generative ML Exploration Techniques

            Latent space exploration

            Prompt generation and improvent

            Optimizers

            Quality-diversity

 

Generative AI State of the Art

            Image generation and synthesis

            Music and sound generation

            Video generation and synthesis

            Multimedia and hybrid models

            Text generation and large language models

 

Computational Creativity in Generative AI

            Introduction

            Creative problem-solving

            Conceptual Blending and other CC techniques

            Evaluation and assessment

 

Applications

            Synthetic Data Generation

            Healthcare

            Design and Art

 

Ethical Considerations, Challenges, Opportunities

Assessment Methods

Assessment
Research work: 30.0%
Project: 70.0%

Bibliography

Foster, D. (2023). Generative Deep Learning (2nd ed.). O’Reilly Media. ISBN: 97810981341811

Bentley, P. J., & Corne, D. W. (Eds.). (2002). Creative Evolutionary Systems. Morgan Kaufmann Publishers

Bentley, P. J. (Ed.). (1999). Evolutionary Design by Computers. Morgan Kaufmann Publishers

Cope, D. (2004). Virtual Music: Computer Synthesis of Musical Style. The MIT Press. ISBN: 97802622559431