The basics of Machine Learning
What is Machine Learning and why should I care?
In this session we will talk about the basic concepts of Machine Learning: Features, Instances, Binary classification, Scoring and Ranking, and beyond.
There are different models used in Machine Learning: logical, geometrical, grouping, probabilistic models, and model ensembles.
We will talk about those models and learn a little bit more about Concept learning, Tree models, Rule models, Linear models, Distance-based models, and probabilistic models.
After we have learn the basics of the models used in Machine Learning we will talk about Experiments in Machine Learning: what to measure, how to measure, and how to interpret the results. And in the end of the session we will take a look to the future: What’s next?