Concept

Loss functions for classification

Related courses (14)

A course on statistical machine learning for supervised and unsupervised learning

This course explores how to design reliable discriminative and generative neural networks, the ethics of data acquisition and model deployment, as well as modern multi-modal models.

PHYS-467: Machine learning for physicists

Machine learning and data analysis are becoming increasingly central in sciences including physics. In this course, fundamental principles and methods of machine learning will be introduced and practi

CIVIL-226: Introduction to machine learning for engineers

Machine learning is a sub-field of Artificial Intelligence that allows computers to learn from data, identify patterns and make predictions. As a fundamental building block of the Computational Thinki

EE-556: Mathematics of data: from theory to computation

This course provides an overview of key advances in continuous optimization and statistical analysis for machine learning. We review recent learning formulations and models as well as their guarantees

DH-406: Machine learning for DH

This course aims to introduce the basic principles of machine learning in the context of the digital humanities. We will cover both supervised and unsupervised learning techniques, and study and imple

ME-390: Foundations of artificial intelligence

This course provides the students with 1) a set of theoretical concepts to understand the machine learning approach; and 2) a subset of the tools to use this approach for problems arising in mechanica

EE-411: Fundamentals of inference and learning

This is an introductory course in the theory of statistics, inference, and machine learning, with an emphasis on theoretical understanding & practical exercises. The course will combine, and alternat

MGT-424: Advanced data driven business analytics

This course aims to provide graduate students a grounding in the methods, theory, mathematics and algorithms needed to apply machine learning techniques to in business analytics domain. The course cov

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-433: Machine learning

Machine learning methods are becoming increasingly central in many sciences and applications. In this course, fundamental principles and methods of machine learning will be introduced, analyzed and pr

MATH-520: Topics in machine learning

Mathematical analysis of modern supervised machine learning techniques, from linear methods to artificial neural networks.

MGT-448: Statistical inference and machine learning

This course aims to provide graduate students a thorough grounding in the methods, theory, mathematics and algorithms needed to do research and applications in machine learning. The course covers topi

CS-233(a): Introduction to machine learning (BA3)

Machine learning and data analysis are becoming increasingly central in many sciences and applications. In this course, fundamental principles and methods of machine learning will be introduced, analy