Concept
Decision tree
Related courses (31)
COM-406: Foundations of Data Science
We discuss a set of topics that are important for the understanding of modern data science but that are typically not taught in an introductory ML course. In particular we discuss fundamental ideas an
DH-415: Ethics and law of AI
This master course enables students to sharpen their proficiency in tackling ethical and legal challenges linked to Artificial Intelligence (AI). Students acquire the competence to define AI and ident
ME-427: Networked control systems
This course offers an introduction to control systems using communication networks for interfacing sensors, actuators, controllers, and processes. Challenges due to network non-idealities and opportun
MGT-418: Convex optimization
This course introduces the theory and application of modern convex optimization from an engineering perspective.
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
MATH-442: Statistical theory
-This course gives a mostly rigourous treatment of some statistical methods outside the context of standard likelihood theory.
MATH-412: Statistical machine learning
A course on statistical machine learning for supervised and unsupervised learning
ME-422: Multivariable control
This course covers methods for the analysis and control of systems with multiple inputs and outputs, which are ubiquitous in modern technology and industry. Special emphasis will be placed on discrete
MGT-529: Data science and machine learning II
This class discusses advanced data science and machine learning (ML) topics: Recommender Systems, Graph Analytics, and Deep Learning, Big Data, Data Clouds, APIs, Clustering. The course uses the Wol
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