Fiches de cours

Adaptation and Learning


Lecturer(s) :

Sayed Ali H.




Every year


Next time: Spring 2019


In this course, students learn to master tools, algorithms, and core concepts related to inference from data, data analysis, and adaptation and learning theories.


The course covers the fundamentals of inference and learning from data, with emphasis on online and adaptive schemes. Students learn about the foundations of adaptive and machine learning techniques in a unified treatment. In particular, the course covers topics related to optimal inference, linear estimation theory, least-squares theory, regularization methods, proximal methods, online and batch methods, stochastic-gradient learning, adaptive filters, generalization theory, Bayes and naive classifiers, nearest-neighbor rules, self-organizing maps, decision trees, logistic regression, discriminant analysis, Perceptron, support vector machines, kernel methods, bagging, boosting, random forests, cross-validation, principal component analysis, neural networks, and adaptive networks. Design projects usually selected from topics related to channel estimation and equalization, echo cancellation, SVM and kernel machines, discriminant analysis, hidden Markov models, deep learning, convolutional networks, and reinforcement learning.

Learning Prerequisites

Recommended courses

Prior exposure to probability theory, random processes, and linear algebra is recommended.

In the programs

Reference week

      Exercise, TP
      Project, other


  • Autumn semester
  • Winter sessions
  • Spring semester
  • Summer sessions
  • Lecture in French
  • Lecture in English
  • Lecture in German