M3-SIMPLE-ML: Introduction to Simple Machine Learning Methods
11h 0min
Review of k-nearest neighbours (as an instance of a distance-based, lazy method); the naïve Bayes classifier (as an instance of a method that considers each feature independently); the decision tree (considers combinations of features); ensembles.
Main Content - Copy
Acquisition
1Lecture content
Content:
– KNN: a distance-based, lazy method;
– Naïve Bayes classifier: considers each feature independently;
– Decision Trees: considers combinations of features;
– Ensembles: what they are, how they work, why they work;
2h 0min
Practice
2Colab Notebooks
A set of colab notebooks, regarding especially these topics:
– Decision tree based classification and regression;
– The impact of pruning hyperparameters: an illustration;
– Ensembles:
– Homogeneous;
– Heterogeneous;
– A naïve Bayes model for text classification;
– ...
3h 0min
0
Investigation
3Independent study time + review
The estimated additional time required for studying the material independently, using the lecture videos/slides and also referencing other literature and material, as necessary. Facilitates correct understanding of the material.
This activity also includes the time required for review before exams.
5h 30min
Assessment
4Quiz activities
Quiz activities meant to provide quick, unassessed feedback to students regarding their grasp of the material.
