Photo Credits: Mateusz Zwierzycki

Syllabus

The course will introduce participants to classification and regression using supervised machine learning. The neural networks will be applied to the specific datasets, which the students will pre-and post-process. The following tasks will be demonstrated:

Dataset preparation Supervised Learning requires high-quality, high-volume datasets to work with. Once the dataset is large enough, neural networks can yield robust and accurate results. During the course, the students will use existing, open-source datasets, as well as prepare their own. The part of the course will focus on the quality of the data as well as methods used to generate datasets. 

Classification – understanding patterns Classification will be useful whenever a binary decision must be made – if a pattern seems like something we want to detect. The pattern can be just about anything – a cat in an image, a word in a recording, or a shape in a parametric model. 

Regression – estimating values While classification uses neural networks for pattern recognition, regression will benefit by using that tool for value estimation. Again, the biggest challenge is to obtain a source dataset. Interpreting results Finally the course participants will learn how to interpret the outputs of a neural network. With many parameters, the “knob-turning” task can be very confusing when training a network. By studying the behaviour of small neural networks in-depth, the students will get an understanding of each of the parameters and how to change them in various scenarios.