- Learning as search, inductive bias
- Combinations of decisions
- Loss function minimization, gradient descent
- Performance assessment
- Instance-based learning
- Probabilistic learning
- Unsupervised classification
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
- 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
- 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”.
The 4 mini-projects worth 30 % of the final grade, 70 % for the exam (closed-book).
The mini-projects can NOT be remade in second session
30 % are already set at the end of Q2 and included as such in the final score in the second session.
- Lectures
- Written assignment and/or Miniproject (2 students/group, from 1 to 3 weeks)
- Assignment feedback
- 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
Required Slides available on:
http://moodleucl.uclouvain.be/course/view.php?id=8900
and more generally all documents (set of mini-projects) available on the same site.
Background:
- LSINF1121 : algorithmics
- LBIR1304 or LFSAB1105 : probability et statistics
