Machine learning is the field of study that gives computers the ability to learn without being explicitly programmed. It is the study and construction of algorithms that can learn from and make predictions on data. Such algorithms operate by building a model from example inputs in order to make data-driven predictions or decisions, rather than following strictly static program instructions. The term "machine learning" is an umbrella term and can be used to describe one of the following topics:
- Classification: Identifying categories an object belongs to.
- Regression: Predict values associated with objects.
- Clustering: Automatic group similar objects into sets.
- Dimension reduction: Make large scale problems more manageable.
- Model selection: Find and calibrate the best models.
- Preprocessing: Normalizing of raw data preparing it for learning algorithms.
In this course we will focus on the application of tools to perform various aspects of machine learning, using the Dlib library. We will touch base on how the various algorithms work.
The course requires familiarity with C++ programming language.
Duration: 4-6 hours
Dlib is a modern C++ toolkit containing machine learning algorithms and tools for creating complex software in C++. The software is open source and cross platform compatible, and also features more general purpose functionality for e.g. linear algebra, image processing, GUI etc. The library supports a number of machine learning algorithms, such as Support Vector Machinesand neural networks.