MGT-529 / 3 crédits

Enseignant(s): Thurm Boris, Vlachos Michail

Langue: Anglais

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

Remark: Only given in 2022-23


Summary

This class discusses advanced data science and machine learning (ML) topics: Recommender Systems, Graph Analytics, and Deep Learning, Big Data, Data Clouds, APIs, Clustering. The course uses the Wolfram Language. Outcome: coding exercises in ML using real-data and a big ML project deliverable.

Content

Keywords

Data science, Machine learning, Algorithms, Big Data, Clustering, Recommender Systems, Deep Learning, Wolfram Language

Learning Prerequisites

Required courses

Data Science and Machine Learning I (MGT-492)

Important concepts to start the course

  • Fundamental Probability and Statistics concepts
  • Fundamental ML topics: cost function and optimization, gradient descent, K-fold cross-validation, overfitting, model calibration, confusion matrix, curse of dimensionality
  • Basic ML methods: Regression (linear regression, ridge regression), Classification (logistic regression, k-NN classification, decision trees, random forests), Dimensionality reduction (PCA, ISOMAP, t-SNE)
  • Basic understanding of Neural Networks and Text Analytics (text representation, sentiment classification, similarity search)
  • Basic programming skills in Wolfram Language

Learning Outcomes

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

  • Choose an appropriate Machine Learning method for a given task
  • Design and Conduct a data science project
  • Investigate data, data types, and problems with the data
  • Implement ML algorithms in Wolfram Language

Transversal skills

  • Plan and carry out activities in a way which makes optimal use of available time and other resources.
  • Demonstrate the capacity for critical thinking
  • Access and evaluate appropriate sources of information.
  • Collect data.
  • Use a work methodology appropriate to the task.

Teaching methods

  • Lectures
  • Lab sessions: coding exercices
  • Data Science projects

Expected student activities

The students are expected to:

  • attend lectures and lab sessions;
  • work on the weekly theory and coding exercises;
  • complete assignments (graded);
  • conduct data science projects making use of the theory learned during lectures and code developed during lab sessions (graded)

Assessment methods

 

  • Coding assignments: 50%
  • Project: 50%

Supervision

Office hours No
Assistants Yes
Forum No
Others Slack channel

Resources

Virtual desktop infrastructure (VDI)

No

Notes/Handbook

Slides will be made available on the course Moodle page. Notebooks will be made available in a GitHub repository.

Book: Introduction to Machine Learning, Etienne Bernard (2022)

https://www.amazon.com/Introduction-Machine-Learning-Etienne-Bernard-ebook/dp/B09PSRXFZ9/

Moodle Link

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 II
  • Cours: 2 Heure(s) hebdo x 14 semaines
  • Exercices: 1 Heure(s) hebdo x 14 semaines

Semaine de référence

 LuMaMeJeVe
8-9     
9-10     
10-11     
11-12     
12-13     
13-14 INM11   
14-15    
15-16 INM11   
16-17     
17-18     
18-19     
19-20     
20-21     
21-22     

Mardi, 13h - 15h: Cours INM11

Mardi, 15h - 16h: Exercice, TP INM11