We teach several courses in the field of artificial intelligence and programming

Programming

The course is an introduction to computer programming following the structured and object oriented paradigms. The used language is Python. The course also introduces recursion and computational complexity, presenting some sorting and searching algorithms.

Course is teached in: Bachelors in Computer Science and Engineering, Computer Science and Engineering + Business Administration, Data Science and Engineering and Data Science and Engineering + Telecommunication Technologies Engineering

Course website: Programming (13868)

Heuristics and Optimization

The objective of this course is to familiarize the student with the fundamental techniques of discrete optimization as well as with the fundamental algorithms for solving satisfiability problems.

Course is teached in: Bachelors in Computer Science and Engineering, Computer Science and Engineering + Business Administration and Applied Mathematics and Computing

Course website: Heuristics and Optimization (15976)

Functional Programming

This course objective is to Acquire the learning outcomes and competencies specified in the "Memoria Verifica" of the title

Course is teached in: Bachelor in Applied Mathematics and Computing

Course website: Functional Programming (18283)

Automatic machine learning (3ECTS)

In this course the aim is to understand basic Machine Learning techniques, learn to determine when to use Machine Learning on real problems, learn to determine which technique is appropriate for each problem and learn to apply the techniques in a practical way to real problems.

Course is teached in: Bachelor in Robotics Engineering

Course website: Automatic machine learning (19101)

Automatic machine learning (6ECTS)

In this course the aim is to understand basic Machine Learning techniques, learn to determine when to use Machine Learning on real problems, learn to determine which technique is appropriate for each problem and learn to apply the techniques in a practical way to real problems.

Course is teached in: Bachelors in Data Science and Engineering and Data Science and Engineering + Telecommunication Technologies Engineering

Course website: Automatic machine learning (16492)

Artificial Intelligence

In this course the fundamentals of Artificial Intelligence techniques will be seen from the conceptual point of view and from the practical point of view.

Course teached in: Bachelors in Computer Science and Engineering, Computer Science and Engineering + Business Administration, Applied Mathematics and Computing and Data Science and Engineering

Course website: Artificial Intelligence (18268)

Computer applications in finance

In this course the objectives are: knowledge of the benefits of using computer applications in finance, identifying a problem of prediction, classification and optimization in finance field, learning how to prepare the financial information for computer processing, understanding the different metrics for evaluating models, knowledge of different computational techniques for prediction, classification and optimization, being able to relate the type of problem with the type of technique, applying computational techniques for solving problems in the financial field, having skills of using computational tools in the financial area, having the ability to evaluate the results obtained through computer applications, being able to properly propose the different phases for the resolution of a problem using the techniques discussed, having the Ability to assess the advantages and disadvantages of using each technique to a specific problem

Course teached in: Bachelor in Finance and Accounting

Course website: Computer applications in finance (13790)

Automatic Planning

This course aims to present state-of-the-art automated planning techniques, characterize every technique as well as the domains they suit better, use tools that implement techniques discussed in class and identify different open issues for research in order to suggest new Master and PhD thesis

Course is teached in: Masters in Applied Artificial Intelligence and Computer Science and Technology

Course website: Automatic Planning (19207)

Search and Optimization

The subject is devoted to the study of the main programming techniques and the design of algorithms (both deterministic and stochastic) for solving discrete optimization tasks, both constructive and traversing the solutions space.

Course is teached in: Master in Applied Artificial Intelligence

Course website: Search and Optimization (19200)

Reinforcement Learning

This course introduces the fundamentals of Reinforcement Learning (RL), covering key concepts, algorithms, and applications. Participants will learn how agents make decisions through trial and error, using techniques such as Q-learning, policy-based methods, and deep reinforcement learning.

Course is teached in: Master in Applied Artificial Intelligence

Course website: Reinforcement Learning (19209)