Coursebook

EDCE - Civil and Environmental Engineering 2019-20    télécharger le plan en pdf

Core courses

Courses
Code
 
Programs
 
Lecturers
Exam
 
Credits
 
 
 
 
 
Active Remote Sensing of the Atmosphere    (Every two years / Next time: Fall 2019. Minimum 5 inscrits)
ENV-716 
EDCE 
Multiple
4
 
 
 
 
Advanced Earthquake Engineering    (Every three years/ Next time: Fall 2020. Minimum 6 inscrits)
CIVIL-706 
EDCE 
Oral
2
 
 
 
 
Climate economics for engineers    (Every two years/ Next time: Fall 2020 Minimum 5 )
ENV-724 
EDCE 
Written
2
 
 
 
 
Environmental Economics for Engineers    (Every two years. Next time spring 2020. Min 5 participants)
ENV-620 
EDCE 
Written
2
 
 
 
 
Fourier analysis and boundary value problems    (Every year/ Next time, Spring 2020)
ENV-614 
EDCE 
Written
4
 
 
 
 
Fracture Mechanics and Fatigue of Structures    (Every two years/ Next time: oct/nov 2019 (Block course). Minimum 5 inscrits)
CIVIL-704 
EDCE 
Oral
2
 
 
 
 
CIVIL-606 
EDCE 
Oral presentation
2
 
 
 
 
Information Science in Engineering    (Variable / Next time: Spring 2020)
CIVIL-711 
EDCE 
Multiple
4
 
 
 
 
Project report
2
 
 
 
 
Models for applied environmental economics    (Every two years/ Next time: Spring 2021 Minimum 5 )
ENV-723 
EDCE 
Oral presentation
1
 
 
 
 
New Concretes for Structures    (Next time fall 2020, Minimum 6)
CIVIL-709 
EDCE 
Oral
2
 
 
 
 
Optimization and simulation    (Every year/ Next time: Spring 2020)
MATH-600 
EDCE 
Multiple
4
 
 
 
 
Performance-based earthquake engineering    (Every two years / Next time: Fall 2020)
CIVIL-714 
EDCE 
Lignos  
Project report
3
 
 
 
 
Scientific programming for Engineers    (Every year / Next time: Fall 2019)
MATH-611 
EDCE 
Project report
4
 
 
 
 
Selected Topics on Advanced Composites in Engineering Structures    (Every two years/ Next time: Spring 2020 Minimum 5)
CIVIL-705 
EDCE 
Multiple
2
 

Other doctoral courses (EDOC)

Courses
Code
 
Programs
 
Lecturers
Exam
 
Credits
 
 
 
 
 
Data Analysis for Science and Engineering    (Postponed until further notice )
MATH-710 
EDMA 
Multiple
4
 
 
 
 
Design of experiments (a) - Fall semester    (Block course Fall 2019 (including a 2 days optional pre-course on Matlab))
ENG-606(a) 
EDRS 
Project report
4
 
 
 
 
Design of experiments (c) - Spring semester    (Next time : June 3 to 19, 2020 (including a half day Matlab training on May 15 am))
ENG-606(c) 
EDRS 
Project report
4
 

External courses (.)

Courses
Code
 
Programs
 
Lecturers
Exam
 
Credits
 
 
 
 
 
Snow Science Winter School (WSL)    (Every year / Next time Feb 2020, for registration see website)
ENV-617 
EDCE 
Schneebeli
Various lecturers  
Project report
3
 
 
 
 
Winter School on Optimization and Operations Research (I)    (Every year/Next time Jan 2020, for registration see website)
MATH-801(1) 
EDCE 
Bierlaire
Various lecturers  
Oral
1
 
 
 
 
Winter School on Optimization and Operations Research (II)    (Information and registration via website: http://transp-or.epfl.ch/zinal/)
MATH-801(2) 
EDCE 
Bierlaire
Various lecturers  
Project report
2
 

Master courses (.)

Courses
Code
 
Programs
 
Lecturers
Exam
 
Credits
 
 
 
 
 
ENG-467 
SIE 
 
 
Written
2
 
 
 
 
Understanding statistics and experimental design    (pas donné en 2019/20 - The course is for MA students and in particular for PhD students.)
BIO-449 
SV 
 
 
Written
4
 

Mandatory courses: You have to choose one of these courses during your first Ph.D year. Be aware that you have to have 4 credits to pass the first year.

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 (3 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.

Design and analysis of experiments in materials science and engineering  MSE-629 (2 credits)

Provide the student with the skills and tools necessary for a wise and efficient orgqnization of his-her experimental work in all fields of materials science and technology (development, processing and caracterization of materials)

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.

Statistiques multivariables avec R   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) – SPRING

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

Information Science in Engineering CIVIL-711 (4 credits) – SPRING (variable)

An introduction to engineering-relevant computer-science concepts that are hardware and software independent. Outcomes include knowledge of the limits of computing, improved ability to understand the true value of new developments and capabilities to effectively select good computing methodologies

Data Analysis for Science and Engineering MATH-710 (4 credits) – POSTPONED

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.

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/ variable

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, ETHZ

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.

.

EDCE courses spring 2020

 

FAQ

  • See the EDCE regulations about credit allocation.
  • No credits can be earned from Bachelor courses
  • Credits from master courses and courses given at other universities must be pre-approved by the thesis director and the director of the doctoral program. The credits will be awarded only after the reception of an official transcript describing explicitly that an exam was passed, and providing the exact number of ECTS credits.
  • Credits can be obtained from short courses or summer schools under the following conditions:
    • The thesis director and the director of the doctoral program must approve the course
    • An official transcript must be provided indicating the number of course hours, plus the number of ECTS credits if available.
    • A formal exam must be passed to evaluate the learning outcomes of the course (a certificate of participation is not sufficient)

Note that the number of ECTS credits proposed by the course organizer does not necessarily correspond to the number of ECTS credits awarded by the doctoral program. The general rule is that a 5-day course is worth 1 ECTS.

  • No credit can be obtained from conferences, seminars, symposium, workshops, internships, etc.