Mandatory courses:

CEE foundations – Statistics – Scientific Programming
Data science – Machine learning – Image processing
Mathematical modelling – ETHZ

You have to choose at least one of these courses during your first PhD year. In special cases, the Program Director may approve courses not included on this list. Be aware that you also need to have 4 credits to pass the first year.
This list may not be updated so please check the course site for exact information.

CEE foundations

Advanced composites in Engineering Structures  CIVIL-443 (3 credits) – FALL

 1. Introduce topics in properties, processing, mechanical behavior, characterization, analysis and structural design of Fiber Reinforced Composites 2. Help students develop their research skills through independent investigations on research topics.

Air pollution ENV-409 (5 credits) – SPRING

A survey course describing the origins of air pollution and climate change.

Composites design and innovation CIVIL-464 (3 crédits) – SPRING

The course offers the opportunity to gain practical experience in the characterization of fiber reinforced polymer and manufacturing/production methods for composite structures. The material is presented by lectures and visits to the laboratory. This is mainly a project based – hands on course

Ecohydrological modeling ENV-411 (4 credits) – SPRING

This course provides the theoretical basis for understanding and modeling the interactions between the hydrologic cycle, vegetation, soil, climate, and human society.

Engineering of existing structures, Civil-511 (4 credits) – FALL

The engineering of existing structures encompasses the examination of condition and load-carrying capacity, decision criteria, and methods for rehabilitation or strengthening. This course presents the bases necessary for this approach at the level of materials and structural response.

Fate and behaviour of environmental contaminants ENV-507 (4 credits) – SPRING

The student will learn the important processes that control the transport and transformation of organic chemicals in the environment, as well as the formulation and solution of quantitative models to describe these processes.

Image processing for Earth observation  ENV-540 (4 credits) – FALL

This course covers optical remote sensing from satellites and airborne platforms. The different systems are presented. The students will acquire skills in image processing and machine/deep learning to extract end-products from the images such as land cover or risk maps

Science of Climate Change ENV-410 ( 4 credits) – Fall

The course equips students with a comprehensive scientific understanding of climate change covering a wide range of topics from physical principles, historical climate change, greenhouse gas emissions, the IPCC assessment to future scenarios and climate action

Structural stability  CIVIL-369 (4 credits) – SPRING

Advanced topics in structural stability; elastic & inelastic column buckling; lateral-torsional buckling of bridge/plate girders; nonlinear geometric effects; frame stability; computational formulation of stability theory; Geometric stiffness method; Plate buckling; Plastic collapse analysis.

Water and wastewater treatment ENV-405 (5 credits) – FALL

This course on water and wastewater treatment shows how to implement and design different methods and techniques to eliminate organic matter, nitrogen and phosporous from wastewater, and how to apply physical and chemical methods and techniques to produce drinking water.

Statistics

Biostatistics  MATH-449: (5 credits) – SPRING

This course covers statistical methods that are widely used in medicine and biology. A key topic is the analysis of longitudinal data: that is, methods to evaluate exposures, effects and outcomes that are functions of time. While motivated by real-life problems, some of the material will be abstract.

Multivariate statistics in R   ENV-513 (4 credits) – FALL

Data required for ecosystem assessment is typically multidimensional. Multivariate statistical tools allow us to summarize and model multiple ecological parameters. This course provides a conceptual introduction and guidelines for the use of multivariate statistical tools using the R platform.

Sensing and spatial modeling for earth observation ENV-408 (5 credits) – SPRING

Students get acquainted with the process of mapping from images (orthophoto and DEM), as well as with methods for monitoring the Earth surface using remotely sensed data. Methods will span from machine learning to geostatistics and model the spatiotemporal variability of processes.

Understanding statistics and Experimental design  BIO-449 (4 credits) – FALL

This course is neither an introduction to the mathematics of statistics nor an introduction to a statistics program such as R. The aim of the course is to understand statistics from its experimental design and to avoid common pitfalls of statistical reasoning. There is space to discuss ongoing work.

Scientific Programming

Scientific programming for engineers  MATH-611 (4 credits) – FALL

The students will acquire a solid knowledge on the processes necessary to design, write and use scientific software. Software design techniques will be used to program a multi-usage particles code, aiming at providing the link between algorithmic/complexity, optimization and program designs.

Data science

Applied data analysis  CS-401 (8 credits) – FALL

This course teaches the basic techniques, methodologies, and practical skills required to draw meaningful insights from a variety of data, with the help of the most acclaimed software tools in the data science world (pandas, scikit-learn, Spark, etc.)

Distributed information systems  CS-423 (6 credits) – FALL

This course introduces the key concepts and algorithms from the areas of information retrieval, data mining and knowledge bases, which constitute the foundations of today’s Web-based distributed information systems.

Systems for data management and data science  CS-460 (8 credits) – SPRING

This course is intended for students who want to understand modern large-scale data analysis systems and database systems. The course covers fundamental principles for understanding and building systems for managing and analyzing large amounts of data. It covers a wide range of topics and technologi

Machine learning

Deep learning EE-559 (4 credits) – SPRING

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.

Machine learning CS-433 (8 credits) – FALL

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, analyzed and practically implemented.

Machine learning 1 MICRO-455 (4 credits) – FALL

Machine learning for Engineers EE-613 (4 credits) – FALL

The objective of this course is to give an overview of machine learning techniques used for real-world applications, and to teach how to implement and use them in practice. Laboratories will be done in python using jupyter notebooks.

Image processing

Image analysis and pattern recognition  EE-451 (4 credits) – SPRING

This course gives an introduction to the main methods of image analysis and pattern recognition.

Visual intelligence: machines and minds CS-503 (6 credits) – SPRING

The course will discuss classic material as well as recent advances in computer vision and machine learning relevant to processing visual data. The primary focus of the course will be on embodied intelligence and perception for active agents.

Mathematical modelling

Mathematical modelling of behaviour  MATH-463 (5 credits) – FALL

Discrete choice models allow for the analysis and prediction of individuals’ choice behavior. The objective of the course is to introduce both methodological and applied aspects, in the field of marketing, transportation, and finance.

Optimization and simulation  MATH-600 (4 credits) – SPRING

Master state-of-the art methods in discrete optimization and simulation. Work involves: – reading the material beforehand – class hours to discuss the material and solve problems – homework

 

Courses at ETHZ

Applied Analysis of Variance and Experimental Design

Summary
Principles of experimental design, one-way analysis of variance, contrasts and multiple comparisons, multi-factor designs and analysis of variance, complete block designs, Latin square designs, random effects and mixed effects models, split-plot designs, incomplete block designs, two-series factorials and fractional designs, power.

Design of Experiments

Summary
The course introduces ‘classical’ statistical design of experiments, particularly designs for blocking, full and fractional factorial designs with confounding, and response surface methods. Topics covered include (restricted) randomization and blocking, sample size and power calculations, confounding, and basics of analysis-​of-variance methods for analysis including random effects and nesting.

FAQ

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EDCE external course list

These are lists of courses followed by oher EDCE Phd students, its main purpose is to help you find interesting courses that you can’t find at EPFL

Statistics

Specialized experimental methods

Economics

General Civil and Environmental Engineering courses

EDCE external transversal skills list

Interesting sites for courses:
Swiss Institute of Bioinformatics
EMBO – Excellence in the life sciences.

This list includes doctoral courses only, master courses are not listed. 
All external courses are subject to approval by the thesis director and the doctoral school. Contact the EDCE office for all external courses.
Listed credits may not correspond to the number of credits awarded by EPFL.
Check if an equivalent course at EPFL exists.
Courses on the list may not longer be taught.