EE-556 / 6 crédits

Enseignant: Cevher Volkan

Langue: Anglais

## Summary

This course reviews recent advances in continuous optimization and statistical analysis along with models. We provide an overview of the emerging learning formulations and their guarantees, describe scalable solution techniques, and illustrate the role of parallel and distributed computation.

## Keywords

Machine Learning. Signal Processing. Optimization. Statististical Analysis. Linear and non-linear models. Algorithms. Data and computational trade-offs.

## Required courses

Previous coursework in calculus, linear algebra, and probability is required. Familiarity with optimization is useful.

## Important concepts to start the course

Previous coursework in calculus, linear algebra, and probability is required.

Familiarity with optimization is useful.

## Learning Outcomes

By the end of the course, the student must be able to:

• Choose an appropriate convex formulation for a data analytics problem at hand
• Estimate the underlying data size requirements for the correctness of its solution
• Implement an appropriate convex optimization algorithm based on the available computational platform
• Decide on a meaningful level of optimization accuracy for stopping the algorithm
• Characterize the time required for their algorithm to obtain a numerical solution with the chosen accuracy

## Assessment methods

Homework assignments. (Continuous control)

## Dans les plans d'études

• Semestre: Automne
• Forme de l'examen: Ecrit (session d'hiver)
• Matière examinée: Mathematics of data: from theory to computation
• Cours: 3 Heure(s) hebdo x 14 semaines
• TP: 3 Heure(s) hebdo x 14 semaines
• Semestre: Automne
• Forme de l'examen: Ecrit (session d'hiver)
• Matière examinée: Mathematics of data: from theory to computation
• Cours: 3 Heure(s) hebdo x 14 semaines
• TP: 3 Heure(s) hebdo x 14 semaines
• Semestre: Automne
• Forme de l'examen: Ecrit (session d'hiver)
• Matière examinée: Mathematics of data: from theory to computation
• Cours: 3 Heure(s) hebdo x 14 semaines
• TP: 3 Heure(s) hebdo x 14 semaines
• Semestre: Automne
• Forme de l'examen: Ecrit (session d'hiver)
• Matière examinée: Mathematics of data: from theory to computation
• Cours: 3 Heure(s) hebdo x 14 semaines
• TP: 3 Heure(s) hebdo x 14 semaines
• Semestre: Automne
• Forme de l'examen: Ecrit (session d'hiver)
• Matière examinée: Mathematics of data: from theory to computation
• Cours: 3 Heure(s) hebdo x 14 semaines
• TP: 3 Heure(s) hebdo x 14 semaines
• Semestre: Automne
• Forme de l'examen: Ecrit (session d'hiver)
• Matière examinée: Mathematics of data: from theory to computation
• Cours: 3 Heure(s) hebdo x 14 semaines
• TP: 3 Heure(s) hebdo x 14 semaines
• Semestre: Automne
• Forme de l'examen: Ecrit (session d'hiver)
• Matière examinée: Mathematics of data: from theory to computation
• Cours: 3 Heure(s) hebdo x 14 semaines
• TP: 3 Heure(s) hebdo x 14 semaines
• Semestre: Automne
• Forme de l'examen: Ecrit (session d'hiver)
• Matière examinée: Mathematics of data: from theory to computation
• Cours: 3 Heure(s) hebdo x 14 semaines
• TP: 3 Heure(s) hebdo x 14 semaines
• Semestre: Automne
• Forme de l'examen: Ecrit (session d'hiver)
• Matière examinée: Mathematics of data: from theory to computation
• Cours: 3 Heure(s) hebdo x 14 semaines
• TP: 3 Heure(s) hebdo x 14 semaines
• Forme de l'examen: Ecrit (session d'hiver)
• Matière examinée: Mathematics of data: from theory to computation
• Cours: 3 Heure(s) hebdo x 14 semaines
• TP: 3 Heure(s) hebdo x 14 semaines

## Semaine de référence

 Lu Ma Me Je Ve 8-9 9-10 MAB111 10-11 11-12 12-13 13-14 14-15 15-16 16-17 BC01BC03BC07-08 17-18 18-19 19-20 20-21 21-22

Vendredi, 16h - 19h: Exercice, TP BC01
BC03
BC07-08

Lundi, 9h - 12h: Cours MAB111