Mandatory courses:

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

You have to choose at least one of these courses during your first PhD year. 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

Spatial statistics and analysis  ENG-440 (5 credits) – FALL

The main objective is to make the students understand the importance of the spatial issues in environmental sciences and engineering, for example for mapping and interpolation. Presentation of different concepts and techniques devoted to spatial data.

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 learning to extract end-products, such as land cover or risk maps, from the images.

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

The objective of the course is to: 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 and climate change ENV-400 (5 credits) – SPRING

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

Fourier analysis and boundary value problems ENV-614 (4 credits) – SPRING

Learning Fourier Series and Boundary Value Problems with a view to a variety of science and engineering problems. Learn the use of special functions like Bessel functions and applications. Introduce the doctoral students to general Sturm-Liouville problems and applications.

Structural stability  CIVIL-369 (4 credits) SPRING

Advanced topics in structural stability; elastic & inelastic column buckling; beam-columns; lateral-torsional buckling of bridge girders; nonlinear geometric effects; frame stability; computational formulation of stability theory; stiffness & flexibility methods

Advanced continuum mechanics  CIVIL-422 (3 credits) FALL

Reading class of classic text book of Lawrence Malvern “Introduction to the Mechanics of a Continuous Medium”. A special emphasis will be put on advanced topics, including finite kinematics, and non-linear material behavior. Applications will cover both solids and structures fluid mechanics.

Statistics

Applied biostatistics  MATH-493 (5 credits) – SPRING

This course covers topics in applied biostatistics, with an emphasis on practical aspects of data analysis using R statistical software. Topics include types of studies and their design and analysis, high dimensional data analysis (genetic/genomic) and other topics as time and interest permit.

Biostatistics  MATH-449: (5 credits) – SPRING

Statistics for life sciences – seems to cover a lot of basic statistic information that was present in the cancelled courses so it may still be relevant.

Multivariate statistics with R in Environment   ENV-521 (4 credits) – FALL

Introduction to multivariate data analysis and modelling. The course helps for a critical choice of methods and their integration in a research planning. It prepares for complexe data analysis in various fields of environemental sciences. Use of dedicated R libraries.

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, including the analysis of results. Modeling aspects, which constrain software design, will lead the students to algorithmic and complexity concepts inherent to all numerical calculation.

Data science

Applied data analysis  CS-401 (6 credits) – FALL

This course teaches the basic techniques and practical skills required to make sense out of 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 (4 credits) – FALL

Information retrieval, data mining and knowledge bases  

Systems for data science  CS-449 (6 credits) – SPRING

Principles for understanding and building systems for managing and analysing large amounts of data – requires the Bachelor unit Introduction to database systems cs-322

Data Analysis for Science and Engineering MATH-710 (4 credits) – Postponed until further notice

Machine learning

Machine learning CS-433 (7 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.

Deep learning EE-559 (4 credits) SPRING

The objective of this course is to provide a complete introduction to deep machine learning. How to design a neural network, how to train it, and what are the modern techniques that specifically handle very large networks.

Applied machine learning  MICRO-455 (4 credits) – FALL

Real-world engineering applications must cope with a large dataset of dynamic variables, which cannot be well approximated by classical or deterministic models. This course gives an overview of methods from Machine Learning for the analysis of non-linear, highly noisy and multi dimensional data.

Machine learning for engineers  EE-613 (EDOC)  (4 credits) – FALL / Next time fall 2021

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.

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.

Mathematical modelling

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

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.

Courses at ETHZ

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. 3h per week (5 credits?).

Applied Analysis of Variance and Experimental Design– 5 credits

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.

Courses in Fribourg

Statistics and experimental design – 3 credits, spring semester

Summary
The course aims to introduce basic concepts of statistics and experimental design. The course will cover topics from the description of data set to multilinear regression analysis.

Introduction to statistics with R – Model selection – 1 credit, spring 2021

Summary
The open source software R (http://www.r-project.org) has revolutionized the statistical data analysis for most bioscience disciplines. R environment covers an unmatched spectrum of statistical tools including an efficient programming language for automating time-consuming analysis routines. Due to its popularity, R is continuously updated and extended with the latest analysis tools that are available in the different research fields. The R environment is completely free and runs on all common operating systems. This course provides a short introduction into the R environment and then tackles in more depth the problem of model selection (the task of selecting a statistical model from a set of candidate models).

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 transferrable 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.