Introduction to Practical Machine Learning

Aim

The overall aim of the course is to provide an introduction to machine learning with a focus on implementing and using models based on neural networks. The course will provide skills in implementing basic deep learning models in a dedicated software library.

After completion of the course, the student should be able to:

1. Use basic concepts and methods in machine learning to formulate, structure, and solve practical problems.

2. Construct basic models for classification and regression.

3. Implement basic neural network-based machine learning models in Python using a deep learning framework and fit these to training data.

4. Use pre-trained machine learning models and analyze their performance.

Content

This course offers a gentle introduction to machine learning with a focus on implementing and using models based on neural networks. This includes: unsupervised and supervised learning; regression and classification; model training, selection, and evaluation; neural networks; convolutional neural network; deep learning; machine learning operations; application of the methods to real data. The course will provide skills in implementing basic deep learning models in a dedicated software environment, including support for GPU acceleration and automatic di􀆯erentiation, which are centralfeatures for practical implementation of deep neural networks.