Machine Learning :classification and evaluation [ LINGI2262 ]
5.0 crédits ECTS
30.0 h + 30.0 h
1q
| Teacher(s) |
Dupont Pierre ;
|
| Language |
English
|
Place
of the course |
Louvain-la-Neuve
|
| Online resources |
> https://www.icampus.ucl.ac.be/claroline/course/index.php?cid=INGI2262
|
| Prerequisites |
Basic knowledge in Probability, Statistics and Algorithmics (as provided by the courses BIR1203, BIR1304 and SINF1121)
|
| 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 |
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.
|
| Evaluation methods |
The 4 mini-projects worth 25% of the final grade, 75% for the exam.
A copy of the slides of course is the only document approved at the final exam.
The mini-projects can NOT be remade in second session
25% are already set at the end of Q1 and included as such in the final score in the second session.
|
| Teaching methods |
-
Lectures
-
Written assignment and/or Miniproject (2 students/group, from 1 to 3 weeks)
-
Assignment feedback
|
| 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
|
| Bibliography |
Required Slides available on:
http://www.icampus.ucl.ac.be/claroline/course/index.php?cid=INGI2262
and more generally all documents (set of mini-projects) available on the same site.
|
Cycle et année
d'étude |
> Master [120] in Computer Science and Engineering
> Master [120] in Computer Science
> Master [120] in Biomedical Engineering
> Master [120] in Statistics: General
> Master [120] in Mathematical Engineering
> Master [120] in Electro-mechanical Engineering
> Master [120] in Electrical Engineering
|
Faculty or entity
in charge |
> INFO
|
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