Study Guide

Objective

The student understands the structure of different types of neural networks and the mathematical methods behind their operation. He acquires the skills needed to create and work with neural networks and the skills involved in programming, data processing, method selection, model construction and interpretation of results, and learns to apply these skills in a variety of machine learning tasks involving e.g. image classification and natural language processing.

Content

- Neural network as a classifier and predictor of numerical values
- Convolutional and feedback neural networks
- Neural network applications

Prerequisites

Data Handling and Machine Learning

Assessment criteria, satisfactory (1)

The student has achieved the course objectives fairly. The student will be able to identify, define and use the course subject area’s concepts and models. The student has completed the required learning exercises in minimum requirement level.

Assessment criteria, good (3)

The student has achieved the course objectives well, even though the knowledge and skills need improvement on some areas. The student has completed the required learning exercises in good or satisfactory level. The student is able to define the course concepts and models and is able to justify the analysis.

Assessment criteria, excellent (5)

The student has achieved the objectives of the course with excellent marks. The student master commendably the course subject area’s concepts and models. The student has completed the required learning exercises in good or excellent level. The student is able to make justified and fluent analysis.

Assessment criteria, approved/failed

The student has achieved the course objectives. The student will be able to identify, define and use the course subject area’s concepts and models. The student has completed the required learning exercises.