DSΕ02

Compulsory

2

Upon successful completion of the course the student will be able to:

• Know a wide range of machine learning methods including the latest and most advanced methods as well as their scope.
• Understand the types of problems solved and the methods that correspond to them.
• Analyze a problem that requires the use of machine learning and apply the appropriate method to it.
• Produce solutions to machine learning problems by applying the most modern software tools

• Search for, analysis and synthesis of data and information, with the use of the necessary technology
• Decision Making
• Teamwork
• Production of free, creative, and inductive thinking

The aim of the course is to provide the student with a comprehensive, up-to-date and in-depth knowledge of the field of machine learning by studying the main modern models, methods and types of learning. Also, basic elements of learning theory are established and the most modern software tools are described. The subject of the course is analyzed in the following sections:

• Support vector machines.
• Deep Learning.
• Deep learning applications
• Bayes modeling and inference
• Ensemble models.
• Probabilistic graphical models.
• Recurrent neural networks.
• Reinforcement Learning.
• Application of Keras/Tensorflow to solve machine learning problems