Welcome to the course "Artificial Intelligence"!
The growing digitization of society is a reality that we have been witnessing, with the adoption of products and technologies that have transformed our personal lives, revolutionizing our relationship with information and communication. At the organization level, digital transformation is also motivated by the dissemination of several innovative technologies, potentially transforming business. This course addresses the main aspects of Artificial Intelligence (AI) and modern Machine Learning (ML) techniques, with a perspective of the impact on modern organizations, contextualizing them in business and organizational scenarios of digital transformation.The course will start by delimiting and defining the concepts of intelligence, AI and ML, followed by an overview of large areas within AI. Problem solving techniques are explored: for decision, search and optimization problems. Knowledge representation, as a key aspect, along with reasoning and uncertainty, is introduced, focusing on up-to-date methods. Besides the fundamental concepts of AI, studied since the 60s, recent developments in machine learning/deep learning and natural language processing (NLP) are introduced, by showing and experimenting with computational systems that are becoming increasingly available.
To develop and consolidate practical skills, challenge-based learning is proposed to the students, with some micro and medium size challenges, founded on real problems, and, if possible, in the context of industry or research partnerships.
On successful completion of this course, graduates will be able to:
- Define what is artificial intelligence and main concepts and questions
- Analyze an intelligent agent, specifying its performance, analyzing environment characteristics and the characterize the different types of agents
- Understand and use of machine learning techniques, such decisions trees, neural networks and k nearest neighborhoods, with real data.
- Apply NLP models through available libraries
Minimal background: basic skills in statistics and computer coding.
Duration: |
1.5 ECTS (40 hours) 5 weeks [13 may - 14 june] |
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- Learn material in the lectures
- Learn from video lectures in each lesson
- Take a test in each of the lessons
- Take a task to solve a challenge of the course
- Use Virtual assistant to get professional answers
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