Data Science and Machine Learning 1: Concepts

Term: 
Winter
Credits: 
2.0
Course Description: 

Type: core

Timing: Friday afternoon + a weekday 5.30

 

After an overview of the entire data science landscape this course will focus on machine learning. The course will introduce the main fundamental concepts in machine learning (supervised learning, training, scoring, accuracy measures, test set, overfitting, cross validation, model capacity, hyperparameter tuning, grid and random search, regularization, ensembles, model selection etc.) The concepts will be illustrated with R code therefore it requires prior familiarity with R.

Learning Outcomes: 

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Assessment: 

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Prerequisites: 

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