UCL - Machine Learning :classification and evaluation

[INGI2262]

[30h + 30h] 5 credits

This course is taught in the 1st semester.

Teacher(s): Pierre Dupont

Language: English

Level: Second

Aims
Main themes
Content and teaching methods
Other information (prerequisite, evaluation (assessment methods), course materials recommended readings, ...)

Aims

- To understand and apply standard techniques to build computer programs that automatically improve with experience,especially for classification problems

- To assess the quality of a learned model for a given task

- To assess the relative performance of several learning algorithms

- To justify the use of a particular learning algorithm given the nature of the data, the learning problem and a relevant performance measure

- To use, adapt and extend learning software

Main themes

- Leaning as search, inductive bias
- Combinations of decisions
- Loss function minimization, gradient descent
- Performance assessment
- Instance-based learning
- Probabilistic learning
- Unsupervised classification

Content and teaching methods

- Decision Tree Learning: ID3, C4.5, CART
- Linear Discriminants: Perceptrons, Gradient-Descent and Least-Square Procedures
- Maximal Margin Hyperplanes and Support Vector Machines
- Performance Measures: Hypothesis testing, Comparing Learning Algorithms
- Gaussian Classfiers, Fisher Linear Discriminants
- Bayesian Learning
- K-Nearest Neighbors
- Clustering Techniques

Other information (prerequisite, evaluation (assessment methods), course materials recommended readings, ...)

- Prerequisites:
(1) SINF1121 or equivalent
(2) Basic knowledge in probability and statistics (BIR1203, BIR1304 or equivalent)

- Evaluation:
Written exam

-Remarque:
WEB site: http://www.info.ucl.ac.be/Enseignant/Cours/INGI2262/