To provide students with knowledge about the concepts, methods, and techniques of machine learning and their role in business. Throughout the course, students will acquire the knowledge necessary for creating and evaluating machine learning models. Specific competencies are developed within seminar classes and through the creation of a seminar paper according to student preferences.
Through the course, students will become familiar with the areas of artificial intelligence application and intelligent methods as a part of intelligent decision support systems. Students will be introduced to machine learning theory, non-parametric learning techniques, methods, regression, and classification—Linear regression, Logistic regression and classification, and regularization. The course will cover artificial neural networks, data clustering techniques (K-means algorithm), dimensionality reduction (Principal Component Analysis), and rule-based learning (Association rules).
After completing the course, the student will be able to:
1. Define basic concepts related to artificial intelligence systems (artificial intelligence, machine learning, learning techniques, data storage)
2. Explain the theoretical assumptions, advantages, and disadvantages of fundamental machine learning algorithms
3. Create (develop) their own machine learning model that uses a supervised, unsupervised, or rule-based learning technique
4. Interpret and evaluate the results of the created machine learning model based on appropriate metrics
