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o Intro to machine learning.
o Intro to R and the R software packages for machine learning
ECUE Big data and machine learning
ECUE's code : IMEBML
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.
Course's manager(s)
In class
- 20h of lectures
PREREQUISITES
Before the start of the course, I must ...
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Attendance to introductory courses on calculus, probability theory and statistics.
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Students are expected to build R coding, thus R programming skills are also strongly recommended.
OBJECTIVES
By the end of this course, I should be able to...
- 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
CONTENT
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Introduction. What is Machine Learning? A conceptual approach
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Supervised 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)
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Unsupervised Learning
o Intro to Unsupervised Learning
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Causality and Machine Learning
No description
Access to complete Syllabus (Authentification required)
Important
This syllabus has no contractual value. Its content is subject to change throughout this year: be aware to the last updates