Study Guide

Objective

The student understands the basic mathematical machinery required in the neural networks.
He/she is able to carry out machine learning projects that include programming, data manipulation, method selection, model building, and evaluation or interpretation of the outcome.

Content

Matrix and differential calculus, programmatic data manipulation, data clustering, classification, numeric prediction, association discovery and text/web mining methods, recommender systems.

Evaluation scale

0-5

Assessment criteria, satisfactory (1)

Students have achieved the course objectives fairly. Students will be able to identify, define and use the course subject area’s concepts and models. The student understands the criteria and principles of the expertise development.

Assessment criteria, good (3)

Students have achieved the course objectives well, even though the knowledge and skills need improvement on some areas. Students are able to define the course concepts and models and are able to justify the analysis. The student is able to apply their knowledge in leisure, study and work situations. The student understands the importance of expertise in the field of information technology and is able to analyze his/her own expertise.

Assessment criteria, excellent (5)

Students have achieved the objectives of the course with excellent marks. Students master commendably the course subject area’s concepts and models. Students are able to make justified and fluent analysis and to present concrete development measures. The students are well prepared to apply their knowledge in leisure, study and work situations. Students are able to analyze the information technology sector expertise and the evolvement of their own expertise.

Assessment criteria, approved/failed

Students have achieved the course objectives. Students will be able to identify, define and use the course subject area’s concepts and models. The student understands the criteria and principles of the expertise development.

Prerequisites

Basic algebra and statistics, Intermediate programming skills, knowledge on handling measurement data.

Further information

The elective course “Neural Networks for Machine Learning Applications” supports this course. It is recommended that the student participates both courses simultaneously.