MGT-492 / 4 crédits

Enseignant(s): Thurm Boris, Vlachos Michail, Schmedders Karl Heinrich

Langue: Anglais

Withdrawal: It is not allowed to withdraw from this subject after the registration deadline.


Summary

This class provides a hands-on introduction to data science and machine learning topics, exploring areas such as data acquisition and cleaning, regression, classification, clustering, neural networks, and visualization. The course consists of lectures and coding sessions using the Wolfram Language.

Content

Keywords

Data science, Machine learning, Algorithms, Regression, Classification, Dimensionality reduction, Neural networks, Textual analysis, Wolfram Language

Learning Prerequisites

Recommended courses

  • Analysis
  • Linear algebra
  • Probability and statistics
  • Econometrics

Important concepts to start the course

  • Basic probability and statistics knowledge (random variables, expectation, mean, conditional and joint distribution, independence, Bayes rule, central limit theorem)
  • Basic linear algebra (matrix/vector multiplication, system of linear equations)
  • Multivariate calculus (derivative w.r.t. vector and matrix variables)
  • Basic programming skills (labs will use Wolfram Language, no need to be familiar with this language)

Learning Outcomes

By the end of the course, the student must be able to:

  • Describe the principal types of machine learning algorithms
  • Investigate data, data types, and problems with the data
  • Explore , clean, and visualize datasets
  • Identify what methods to use for a given problem
  • Implement machine learning algorithms in the Wolfram Language
  • Optimize the main tradeoffs such as overfitting and computational cost vs accuracy

Transversal skills

  • Evaluate one's own performance in the team, receive and respond appropriately to feedback.
  • Plan and carry out activities in a way which makes optimal use of available time and other resources.
  • Demonstrate the capacity for critical thinking

Teaching methods

  • Lectures
  • Exercice sessions
  • Group projects

Expected student activities

  • attend lectures and lab sessions
  • work on the weekly theory and coding exercises
  • complete assignments (graded)
  • collaborate on group projects (3 students) making use of the theory learned during lectures and code developed during lab sessions (graded)

Assessment methods

  • Quizzes: 30% (personal)
  • Assignments: 35% (personal)
  • Group project: 35% (3 students)

 

Supervision

Office hours No
Assistants Yes
Forum No
Others Slack channel

Resources

Virtual desktop infrastructure (VDI)

No

Bibliography

  • [not mandatory] Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking, by Foster Provost and Tom Fawcett

Ressources en bibliothèque

Websites

Moodle Link

Prerequisite for

Data science and machine learning II

Dans les plans d'études

  • Semestre: Automne
  • Nombre de places: 40
  • Forme de l'examen: Pendant le semestre (session d'hiver)
  • Matière examinée: Data science and machine learning I
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Exercices: 2 Heure(s) hebdo x 14 semaines

Semaine de référence

 LuMaMeJeVe
8-9     
9-10     
10-11  CO120  
11-12    
12-13     
13-14     
14-15     
15-16     
16-17CO120    
17-18    
18-19     
19-20     
20-21     
21-22     

Mercredi, 10h - 12h: Exercice, TP CO120

Lundi, 16h - 18h: Cours CO120