Course

EE-608: Deep Learning For Natural Language Processing

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COM-490: Large-scale data science for real-world data

This hands-on course teaches the tools & methods used by data scientists, from researching solutions to scaling up prototypes to Spark clusters. It exposes the students to the entire data science pipe

EE-566: Adaptation and learning

In this course, students learn to design and master algorithms and core concepts related to inference and learning from data and the foundations of adaptation and learning theories with applications.

CS-101: Advanced information, computation, communication I

Discrete mathematics is a discipline with applications to almost all areas of study. It provides a set of indispensable tools to computer science in particular. This course reviews (familiar) topics a

BIOENG-456: Controlling behavior in animals and robots

Students will acquire an integrative view on biological and artificial algorithms for controlling autonomous behaviors. Students will synthesize and apply this knowledge in oral presentations and comp

MICRO-455: Machine learning I

NX-421: Neural signals and signal processing

Understanding, processing, and analysis of signals and images obtained from the central and peripheral nervous system

ME-213: Programmation pour ingénieur

Mettre en pratique les bases de la programmation vues au semestre précédent. Développer un logiciel structuré. Méthode de debug d'un logiciel. Introduction à la programmation scientifique. Introductio

MATH-412: Statistical machine learning

A course on statistical machine learning for supervised and unsupervised learning

CS-119(h): Information, Computation, Communication

L'objectif de ce cours est d'initier les étudiants à la pensée algorithmique, de les familiariser avec les fondamentaux de l'informatique et des communications et de développer une première compétence

CS-526: Learning theory

Machine learning and data analysis are becoming increasingly central in many sciences and applications. This course concentrates on the theoretical underpinnings of machine learning.