Course

Applied Statistics and Machine Learning with Subsurface Applications (E-MOD323)

One of the four-micro-course program created for professionals who need to incorporate Python programming in their daily work, but who have little or no background in Python.


Dette er emnebeskrivelsen for studieåret 2025-2026

Fakta

Emnekode

E-MOD323

Vekting (stp)

2

Semester undervisningsstart

Spring

Undervisningsspråk

English

Antall semestre

1

Vurderingssemester

Spring

Timeplan

Vis timeplan

Litteratur

Søk etter pensumlitteratur i Leganto

Content

This micro-course is the third in a four-micro-course program created for professionals who need to incorporate python programming in their daily work, but who have little or no background in Python. At the end of this course, students will have a comprehensive overview of the fundamental concepts upon which applied statistics, machine learning, and artificial intelligence are based and the differences between these methods. They will have the opportunity to apply these methods to domain-specific areas (e.g. well logs 1D and seismic data 2-3D).

Learning outcome

a At the end of the course, students can:

i Differentiate between applied statistics, machine learning, and artificial intelligence.

ii Apply statistical tools, such as numpy, scipy, and pandas on real world examples (e.g. subsurface and engineering problems).

iii Apply advanced machine learning libraries, sklearn and pytorch on real world examples (e.g. subsurface and engineering problems).

iv Develop awareness of how to apply these techniques.

v Acquire the basic concept and relative vocabulary for applied statistics, machine learning, and artificial intelligence.

Forkunnskapskrav

Ingen

Anbefalte forkunnskaper

All previous micro-courses (none for module 1).

Exam

Form of assessment Weight Duration Marks Aid
Home exam 1/1 2 Weeks Passed / Not Passed All


The assignment is a practical coding project. The students will submit the code with internal documentation and the output with a short explanation.

Re-submission of project follows the next offering of the course; re-enrollment is not required.

Fagperson(er)

Course teacher:

Aksel Hiorth

Course teacher:

Nestor Fernando Cardozo Diaz

Course coordinator:

Enrico Riccardi

Method of work

a This is a project-based course where the students learn concepts and their application to practical problems. We use multiple forms of dissemination: the student learns from teaching material prior to the class. In the class, physical teaching is mixed with guided coding activities during 2 days. After the class, virtual meetings are held to discuss the student coding project.

b Teaching material available prior to the course meeting

c 2 days physical teaching with integrated lectures and labs

d Python programming on laptop/computer during meetings and at home

e One virtual meeting during the micro-course to assist with the project

f Virtual meeting at the end of the micro-course to discuss the project outcomes

Åpent for

Emneevaluering

There must be an early dialogue between the course supervisor, the student union representative and the students. The purpose is feedback from the students for changes and adjustments in the course for the current semester.In addition, a digital course evaluation must be carried out at least every three years. Its purpose is to gather the students experiences with the course.

Kontakt

Course teacher:

Aksel Hiorth

Course teacher:

Nestor Fernando Cardozo Diaz

Course coordinator:

Enrico Riccardi

Tilbys av

Faculty of Science and Technology

Department of Energy Resources

The course description is retrieved from FS (Felles studentsystem). Version 1