SkillsCourses

Basics of Data Analytics

2 Modules

30 Minutes

EQF-Level: 5

About

Learning Outcomes

Modules

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About the course

Target group

Physicians, Technicians 

Key words

,

Course introduction

Definition of Machine Learning and of the different classes of problems. General survey of the main methodologies used to handle medical data: 

  • Under Supervised Learning: linear regression; logistic regression; neural networks; decision trees and random forests; support vector machine; splines; k-NN. 
  • Under Unsupervised Learning: k-MEANS. 

Problems associated with these methodologies: overfitting and cross validation. 

Details to know

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Assessment

6 Quizzes

Taught in English

Only available in English

Learning outcomes

Module 1: Fundamentals / Module 2: Approaches 

  • Competence 
    • Ability to productively use simple Machine Learning approaches in their work tasks. 
  • Knowledge 
    • Understanding the difference between classification and clustering. 
    • Understanding the difference between supervised and unsupervised ML approaches. 
    • Understanding overfitting and cross-validation. 
    • Basic knowledge of linear regression. 
    • Basic knowledge of other supervised ML approaches. 
  • Skills 
    • Selecting the appropriate approach required in practical settings. 
    • Preventing overfitting issues in ML approaches. 
    • Using linear regression tools. 
    • Using other major supervised approaches.  

More detailed Learning Outcomes can be found in module introductions.

Fundamentals

Module 1: The module will first deal with the definition of Machine Learning and of the different classes of problems. Then, linear regression will be analyzed as a simple tool to understand Machine Learning. Finally, the main problems associated with these methodologies will be discussed; in particular, the overfitting issue will be studied together with cross validation strategies to overcome it.

Lessons

Introduction 1. Artificial intelligence and machine learning 2. Linear regression at work 3. Introduction to overfitting and cross-validation

Approaches

Module 2: In this module we will overview Supervised Machine Learning approaches: (including linear regression; logistic regression; neural networks; decision trees and random forests; support vector machine; splines; k-NN) and one Unsupervised Machine Learning approach (i.e., the k-MEANS). 

Lessons

Introduction 1. Overview of other supervised approaches (1) 2. Overview of other supervised approaches (2) 3. k-NN, clustering, simulated and real data Course evaluation

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Co-funded by the Erasmus+ programme of the European Union under Grant Agreement number 101056563.

Co-funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or EACEA. Neither the European Union nor the granting authority can be held responsible for them.