Programme

The course aims to provide advanced and specific competencies in recent machine learning models and technologies. The course will enable the solving of complex problems through appropriate problem formulation and definition of the most suitable models and knowledge representations, and the most efficient implementation techniques for machine learning algorithms. Reinforcement learning and state-of-the-art models, such as graph neural networks and tuning and self-tuning techniques, will be introduced.
The course consists of a theoretical and methodological part on advanced and innovative concepts, and a laboratory activity in which these concepts are applied in problem solving using the latest development frameworks.

Core Documentation

Lecture notes by the professor.

Attendance

EN: Attendance is not compulsory

Type of evaluation

Written test, practical test.

Programme

The course aims to provide advanced and specific competencies in recent machine learning models and technologies. The course will enable the solving of complex problems through appropriate problem formulation and definition of the most suitable models and knowledge representations, and the most efficient implementation techniques for machine learning algorithms. Reinforcement learning and state-of-the-art models, such as graph neural networks and tuning and self-tuning techniques, will be introduced.
The course consists of a theoretical and methodological part on advanced and innovative concepts, and a laboratory activity in which these concepts are applied in problem solving using the latest development frameworks.

Core Documentation

Lecture notes by the professor.

Attendance

EN: Attendance is not compulsory

Type of evaluation

Written test, practical test.

Programme

The course aims to provide advanced and specific competencies in recent machine learning models and technologies. The course will enable the solving of complex problems through appropriate problem formulation and definition of the most suitable models and knowledge representations, and the most efficient implementation techniques for machine learning algorithms. Reinforcement learning and state-of-the-art models, such as graph neural networks and tuning and self-tuning techniques, will be introduced.
The course consists of a theoretical and methodological part on advanced and innovative concepts, and a laboratory activity in which these concepts are applied in problem solving using the latest development frameworks.

Core Documentation

Lecture notes by the professor.

Attendance

EN: Attendance is not compulsory

Type of evaluation

Written test, practical test.

Programme

The course aims to provide advanced and specific competencies in recent machine learning models and technologies. The course will enable the solving of complex problems through appropriate problem formulation and definition of the most suitable models and knowledge representations, and the most efficient implementation techniques for machine learning algorithms. Reinforcement learning and state-of-the-art models, such as graph neural networks and tuning and self-tuning techniques, will be introduced.
The course consists of a theoretical and methodological part on advanced and innovative concepts, and a laboratory activity in which these concepts are applied in problem solving using the latest development frameworks.

Core Documentation

Lecture notes by the professor.

Attendance

EN: Attendance is not compulsory

Type of evaluation

Written test, practical test.

Programme

The course aims to provide advanced and specific competencies in recent machine learning models and technologies. The course will enable the solving of complex problems through appropriate problem formulation and definition of the most suitable models and knowledge representations, and the most efficient implementation techniques for machine learning algorithms. Reinforcement learning and state-of-the-art models, such as graph neural networks and tuning and self-tuning techniques, will be introduced.
The course consists of a theoretical and methodological part on advanced and innovative concepts, and a laboratory activity in which these concepts are applied in problem solving using the latest development frameworks.

Core Documentation

Lecture notes by the professor.

Attendance

EN: Attendance is not compulsory

Type of evaluation

Written test, practical test.