Machine Learning :classification and evaluation

lingi2262  2019-2020  Louvain-la-Neuve

Machine Learning :classification and evaluation
Note from June 29, 2020
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
5 credits
30.0 h + 30.0 h
Q2
Teacher(s)
Dupont Pierre;
Language
English
Prerequisites
Main themes
  • Learning as search, inductive bias
  • Combinations of decisions
  • Loss function minimization, gradient descent
  • Performance assessment
  • Instance-based learning
  • Probabilistic learning
  • Unsupervised classification
Aims

At the end of this learning unit, the student is able to :

1
Given the learning outcomes of the "Master in Computer Science and Engineering" program, this course contributes to the development, acquisition and evaluation of the following learning outcomes:
  • INFO1.1-3
  • INFO2.3-4
  • INFO5.3-5
  • INFO6.1, INFO6.4
Given the learning outcomes of the "Master [120] in Computer Science" program, this course contributes to the development, acquisition and evaluation of the following learning outcomes:
  • SINF1.M4
  • SINF2.3-4
  • SINF5.3-5
  • SINF6.1, SINF6.4
Students completing successfully this course will be able to:
  • understand and apply standard techniques to build computer programs that automatically improve with experience, especially for classification problems
  • assess the quality of a learned model for a given task
  • assess the relative performance of several learning algorithms
  • justify the use of a particular learning algorithm given the nature of the data, the learning problem and a relevant performance measure
  • use, adapt and extend learning software
Students will have developed skills and operational methodology. In particular, they have developed their ability to:
  • use the technical documentation to make efficient use of existing packages,
  • communicate test results in a short report using graphics.
 

The contribution of this Teaching Unit to the development and command of the skills and learning outcomes of the programme(s) can be accessed at the end of this sheet, in the section entitled “Programmes/courses offering this Teaching Unit”.
Content
  • Decision Tree Learning: ID3, C4.5, CART, Random Forests
  • Linear Discriminants: Perceptrons, Gradient-Descent and Least-Square Procedures
  • Maximal Margin Hyperplanes and Support Vector Machines
  • Probability and Statistics in Machine Learning
  • Performance Assessment: Hypothesis testing, Comparing Learning Algorithms, ROC analysis
  • Gaussian Classifiers, Fisher Linear Discriminants
  • Bayesian Learning: ML, MAP, Optimal Classifier, Naive Bayes
  • Instance-based learning: k-NN, LVQ
  • Clustering Techniques
Teaching methods
  • Lectures
  • Several mini-projects (from 1 to 3 weeks) including some theoretical questions and mostly practical applications
  • The mini-projects are, by default, implemented in R and evaluated semi-automatically on a server (INGInious)
  • An R tutorial is included
Evaluation methods
The mini-projects are worth 20 % of the final grade, 80 % for the final exam (closed-book).
The mini-projects can NOT be implemented again in second session.
The 20 % for the mini-projects are fixed at the end of the semester and included as such in the global score for the second session.
The final exam is, by default, a written exam (on paper or, when appropriate, on a UCLouvain computer).
Online resources
http://moodleucl.uclouvain.be/course/view.php?id=8900
Bibliography
Des ouvrages complémentaires sont recommandés sur le site Moodle du cours.
Additional textbooks are recommended on the Moodle site for this course.
Teaching materials
  • Les supports obligatoires sont constitués de l'ensemble des documents (transparents des cours magistraux, énoncés des travaux pratiques, compléments, ...) disponibles sur le site Moodle du cours.
  • Required teaching material include all documents (lecture slides, project assignments, complements, ...) available on the Moodle website for this course.
Faculty or entity
INFO


Programmes / formations proposant cette unité d'enseignement (UE)

Title of the programme
Sigle
Credits
Prerequisites
Aims
Master [120] in Biomedical Engineering
GBIO2M
5
Master [120] in Data Science : Statistic
DATS2M
5
Master [120] in Mathematical Engineering
MAP2M
5
Master [120] in Computer Science and Engineering
INFO2M
5
Master [120] in Electrical Engineering
ELEC2M
5
Master [120] in Computer Science
SINF2M
5
Master [120] in Data Science Engineering
DATE2M
5
Master [120] in Statistic: Biostatistics
BSTA2M
5
Certificat d'université : Statistique et sciences des données (15/30 crédits)
STAT2FC
5
Master [120] in Data Science: Information Technology
DATI2M
5
Master [120] in Statistic: General
STAT2M
5