Course Summary

This course unit provides students with a comprehensive understanding of Artificial Intelligence (AI) and Machine Learning (ML), equipping them with the skills to tackle real-world decision-making problems. It covers foundational topics in AI, including informed and uninformed search algorithms, optimization techniques, progressive improvement algorithms, heuristic methods, automatic planning, and applications in diverse domains. Students will also master the evaluation, implementation, and optimization of ML models for supervised, unsupervised, and reinforcement learning scenarios, applying them in classification, regression, and clustering tasks.

Course Highlights

• Fundamentals of Artificial Intelligence and Problem-Solving Techniques
• Informed and Uninformed Search Algorithms
• Progressive Improvement and Heuristic Algorithms
• Automatic Planning and Decision Support Systems
• Supervised, Unsupervised, and Reinforcement Learning
• Deep Learning and Neural Network Architectures

Instructors

• Prof. Artur Jorge Ferreira
• Prof. José Manuel Peixoto Nascimento

Learn More

For a detailed syllabus, learning outcomes, assessment methods, and recommended literature, please refer to the official course documentation provided.