Speaker
Description
Syllabus outline:
Concept of Machine Learning and application fields. Slides: 15 minutes
Supervised and unsupervised learning. Slides: 20 minutes
Approaching a problem of learning from examples. Slides: 25 minutes
First (simple) classifier: K-Nearest Neighbours (K-NN). Slides: 10 minutes
Another classifier: Naïve Bayes. Slides: 20 minutes
Evaluating classifiers’ performance. Slides: 30 minutes
Objective competences:
Comprehensive overview about machine learning basic concepts.
Understanding the fundamentals of training a classifier.
Basic knowledge about how to evaluate a classifier and how to interpret its results
Intended learning outcomes:
To know the basic knowledge about machine learning.
To understand the process of training a classifier
To know about two basic supervised learning classifiers: kNN and Naïve Bayes
To evaluate and interpret classification model results.
