Study Guide

Objective

After completing the course, the student masters the most typical machine learning techniques and understands their utilization possibilities. In addition to theoretical understanding, the student is able to apply the methods he/she has learned to solving practical problems and has his/her own basic view of good practices related to the implementation of machine learning and artificial intelligence applications.

Content

- Introduction to Machine Learning
- Typical steps of the workflow
- Basics of data processing (Z-score, Box-Cox, etc.)
- Measurement of model performance (MSE, F1, etc.)
- Several different algorithms, such as:
- Naive Bayes
- Decision tree
- k-NN
- k-Means
- Linear Regression (Hill Climbing and/or Gradient Descent)

Evaluation scale

0 - 5

Assessment criteria, excellent (5)

The student uses the concepts of his/her professional field competently and extensively and combines them into wholes. The student can analyze, reflect and critically evaluate his/her own competence and the operating methods of his/her professional field with the help of the knowledge he/she has acquired. The student also knows how to select and critically evaluate the techniques and models of their professional field and use them in their activities and critically apply professional ethical principles in their activities.

Assessment criteria, good (3)

The student consistently uses the concepts of his/her professional field and knows how to name, describe and justify the basic information of his/her professional field. The student chooses appropriate methods of operation based on the knowledge and instructions he/she has acquired, and appropriately applies techniques and models suitable for the operation of his professional field. The student evaluates and reflects on his/her own competence and knows how to justify his/her actions in accordance with professional ethical principles.

Assessment criteria, satisfactory (1)

The student uses the key concepts of the course's subject area appropriately and knows how to name the basic information of his/her professional field. The student acts appropriately, although the action may still be uncertain and requires guidance. The student appropriately uses the techniques and models of his/her professional field in his/her activities and acts in accordance with professional ethical principles.

Prerequisites

Knowledge of Git, Python and Jupyter Notebook must be at least at the basic level.