- Learning as search, inductive bias
- Combinations of decisions
- Loss function minimization, gradient descent
- Performance assessment
- Instance-based learning
- Probabilistic learning
- Unsupervised classification
At the end of this learning unit, the student is able to :
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:
Students will have developed skills and operational methodology. In particular, they have developed their ability to:
- 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
Due to the COVID-19 crisis, the information in this section is particularly likely to change.
- Several projects including some theoretical questions and mostly practical applications.
Practical projects are submitted on line and evaluated on the Inginious platform.
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.
Additional textbooks are recommended on the Moodle site for this course.
- 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.
- project 1 = 15%
- project 2 = 15%
- project 3 = 10%
- project 4 = 15%
- project 5 = 45%