Machine Learning :classification and evaluation

lingi2262  2020-2021  Louvain-la-Neuve

Machine Learning :classification and evaluation
Due to the COVID-19 crisis, the information below is subject to change, in particular that concerning the teaching mode (presential, distance or in a comodal or hybrid format).
6 credits
30.0 h + 30.0 h
Q2
Teacher(s)
Language
English
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
  • Deep Learning
  • 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
Teaching methods

Due to the COVID-19 crisis, the information in this section is particularly likely to change.

  • Lectures
  • Several projects including some theoretical questions and mostly practical applications. 
By default, lectures can be followed face to face in the auditorium announced in the official schedule. Depending on the number of registered students and the evolution of the sanitary situation, students will be able to follow the lectures as well remotely on Teams.
 Practical projects are submitted on line and evaluated on the Inginious platform.
 
Evaluation methods

Due to the COVID-19 crisis, the information in this section is particularly likely to change.

The projects are worth 40 % of the final grade, 60 % for the final exam (closed-book).
The mini-projects cannot be implemented again in second session.
The projects grades 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 computer)
These evaluation rules are subject to possible updates due to the sanitary situation. In particular, the relative weights between the projects and the final exam could be adapted.  Such possible updates would be notified to the students by a general announcement  posted on the Moodle site of this course.
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 depuis le site Moodle du cours.
  • Required teaching material include all documents (lecture slides, project assignments, complements, ...) available from the Moodle website for this course.
Faculty or entity
Force majeure
Teaching methods
Lectures are given online and can be followed remotely. Computing projects are submitted online on the Inginious platform.
Evaluation methods
The gobal grade for the course is only based on the projects (no final exam). The relative weights of the projects in the global grade is adapted as follows:
  • project 1 = 15%
  • project 2 = 15%
  • project 3 = 10%
  • project 4 = 15%
  • project 5 = 45%   
In case of a second session, a new version of project 5 is implemented and re-evaluated. The grades of the other projects are fixed, after a feedback has been given, and cannot be re-implemented.


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

Title of the programme
Sigle
Credits
Prerequisites
Aims
Master [120] in Data Science : Statistic

Master [120] in Computer Science and Engineering

Master [120] in Computer Science

Certificat d'université : Statistique et sciences des données (15/30 crédits)

Master [120] in Electrical Engineering

Master [120] in Mathematical Engineering

Master [120] in Data Science Engineering

Master [120] in Data Science: Information Technology

Master [120] in Statistic: General

Master [120] in Statistic: Biostatistics

Master [120] in Biomedical Engineering

Master [60] in Computer Science