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

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.

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.

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 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