Machine Learning :classification and evaluation

Teacher(s)

Dupont Pierre;

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

Learning outcomes

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.

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

• Lectures
• Computing projects including theoretical questions and practical applications. These projects are implemented in python. They are submitted and evaluated on the Inginious platform.

Evaluation methods

For the first session, the global grade for the course is solely based on the grades of the computing projects, submitted and evaluated during the semester.
This global grade is computed as a weighted average of the project grades according to the following weighting scheme:

• project 1 = 10%
• project 2 = 15%
• project 3 = 10%
• project 4 = 15%
• project 5 = 50%

The projects are not evaluated again for the second session and may not be resubmitted. The grades for projects 1 to 4 are kept as such, while project 5 is replaced by a closed book written exam. This written exam is by default on paper or, when appropriate, on a computer. The global grade is computed according to the same weighting scheme used for the first session, with the written exam representing 50% of this global grade (and replacing the project 5 grade).

Online resources

moodle.uclouvain.be/course/view.php?id=1836

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

INFO