Artificial Intelligence

We have obtained the endorsement of the University of California, Berkeley to explore and exploit the material of the course "CS188 Artificial Intelligence", taught at one of the best universities in the world. 🌍🔬

We received the exciting news that DataCamp has verified and accepted our syllabus. 🤝✨ This means that all students will be able to access the prestigious material of this renowned cloud training company specialized in AI! 💪🤖 With the collaboration of DataCamp, we are creating tracks that will cover the skills necessary for development in this exciting field. 🚀⌨️

Course Description:

This course provides an in-depth study of the theories, algorithms, and applications of artificial intelligence (AI). The course covers a broad range of topics in AI, including intelligent agents, problem-solving, search algorithms, probability, and machine learning. 

It then covers the basic concepts of AI, including machine learning, reinforcement learning, decision trees, neural networks, and Bayesian learning.

The course emphasizes hands-on experience with AI tools and frameworks using Python.

Upon completion of the course, students will have a comprehensive understanding of AI theory and practice, and be able to apply this knowledge to real-world problems in a variety of industries. The course is designed for students with a strong background in computer science or related fields, and requires a solid foundation in calculus, linear algebra, probability theory, and Python.

The course begins by introducing the basic concepts of AI, including problem-solving, search algorithms, and heuristic techniques. It then covers the fundamental principles of machine learning, including supervised and unsupervised learning, decision trees, and neural networks. The course also explores natural language processing, including text processing, sentiment analysis, and machine translation.

Another important aspect of the course is the study of robotics, including the basic principles of kinematics, dynamics, and control of robotic systems. The course also introduces computer vision, including image processing, feature extraction, and object recognition.

The course emphasizes hands-on experience with AI tools and frameworks, including Python programming language. Students will have opportunities to apply the concepts and techniques learned in the course to real-world problems through projects and assignments.

Upon completion of the course, students will have a solid understanding of the foundational concepts of AI and the ability to apply them to solve real-world problems. The course is designed for undergraduate students with a strong background in computer science and mathematics, and no prior knowledge of AI is required.

Course contribution to professional training:

This course offers a valuable contribution to professional training by equipping individuals with the skills and knowledge necessary to work with and leverage the power of AI technologies in various industries.

This course offers numerous technical contributions to the professional field of graduate students, including:

Course objectives:

This course aims to provide students with a comprehensive understanding of AI and its applications. Some of the key course objectives are:

Curricular Units:


Course Overview


Introduction to Artificial Intelligence 


Informed and Local Search

UC. 3 

Logic and Planning

UC. 4

Constraint Satisfaction Problems

UC. 5 

Search Games 

UC. 6

Markov Decision Processes

UC. 7

Reinforcement Learning

UC. 8

Bayes Networks I

UC. 9

Bayes Networks II

UC. 10

Markov Models and Filtering

UC. 11


UC. 12

Decision Networks

UC. 13

MLP and Neural Networks

UC. 14

Ethics and Advanced Topics on AI