Objectives
The goal of the course is to have a broad introduction on data science and artificial intelligence techniques. The course is split into three parts:
- Introduction to Data Science, in which we learn the why data is the value and what are the existing challenges that needs mining of the data.
- Unsupervised learning, in which we study the concept and some of the related algorithms: hierarchical clustering, kmeans, dbscan, hdbscan, etc.
- Supervised learning, in which we study the concept and some of the related algorithm: regression (linear and logistic), decision trees, Naïve Bayes, SVM, random forest
- Text analysis (supervised and unsupervised) in which we will review the specificities of text analysis
Each course is followed by practical work using R and/or python
Organisation
14 hour coursework, 20 hours practice
Evaluation
Practical session grading
Return to Coursework.
Last modify 29 January 2019