Université Côte d'azur
/ Formation / Syllabus IMEBML
Français English

ECUE Big data and machine learning

Code de l'ECUE : IMEBML

Ce cours est proposé dans 3 UE
EUR ELMI
Sciences de gestion et du management
Campus Saint Jean d'Angély
Master 1
Semestre pair
Anglais

PRESENTATION

Big data analysis is most commonly associated with ”machine learning” techniques and algorithms:

This course adopts an introductory but broad perspective on machine learning opportunities and solutions to successfully operate in the field of big data analysis for the economic and finance profession.

In this course students will learn about the core concepts in machine learning, as well as train the skills necessary to apply these methods widely and develop their programming abilities in the R language. Finally, they will familiarize themselves with the applied literature in the topic.

This is an introductory course, so the lectures and problem sets will be focused on the intuition and the mechanics behind machine learning concepts rather than the mathematical fundamentals. This course covers the mechanics underlying machine learning methods and discusses how these techniques can be leveraged by social scientists to gain new insight from their data. Specifically, the course will cover: decision trees, random forests, boosting, k-means clustering and nearest neighbours, support vector machines, kernels, neural networks, and ensemble learning. We will also discuss topics related to best practices, including error rates, cross-validation, and the use of bootstrapping methods to develop uncertainty estimates.

Responsable(s) du cours

Présentiel

  • 20h de cours magistral

PREREQUIS

Avant le début du cours, je dois ...
  • Attendance to introductory courses on calculus, probability theory and statistics.
  • Students are expected to build R coding, thus R programming skills are also strongly recommended.

OBJECTIFS

A la fin de ce cours, je devrais être capable de...
  • familiarize themselves with the applied literature in the topic
  • the core concepts in machine learning
  • apply these methods widely and develop their programming abilities in the R language

CONTENU

  • o Intro to machine learning.

    o Intro to R and the R software packages for machine learning

  • o Intro to Classification and Regression Analysis

    o Comparing classification Methods

    o Intro to regularization

    o Tree models

    o Bagging / Random forests

    o Neural networks

    o Support Vector Machine (L1/L2 regularization)

  • o Intro to Unsupervised Learning

  • Aucune description
Accéder au Syllabus complet (Authentification requise)
Important
Ce syllabus n’a aucune valeur contractuelle. Son contenu est susceptible d’évoluer en cours d’année : soyez attentifs aux dernières modifications.