Objective
The course aims at familiarizing the audience with fundamental machine learning techniques and algorithms that cover the spectrum of diverse machine learning applications (supervised / unsupervised learning).
The expected learning outcome of the course includes knowledge of the basic machine learning methods and gaining of experience in implementing and using them effectively.
It also includes the critical ability for the choice of an appropriate methodology for each distinct machine learning problem, along with the deep understanding of its advantages and weaknesses.
After successfully completing the course, students will be able to:
- understand basic machine learning methods and algorithms
- distinguish between supervised and unsupervised learning problems
- choose correct classifiers, feature selection methods,data transformations, and clustering algorithms
- design and implement machine learning methods
- evaluate the results of applying machine learning algorithms
Learning outcomes
- Search for, analysis and synthesis of data and information, with the use of the necessary technology
- Adapting to new situations
- Decision-making
- Working independently
- Team work
- Production of new research ideas
- Project planning and management
- Criticism and self-criticism
- Production of free, creative and inductive thinking
