Artificial Intelligence for Engineers DAT305

In the rapidly evolving and complex field of engineering, engineers face the challenge of understanding the AI landscape within engineering applications, navigating ethical considerations, scoping AI projects, and identifying AI use cases within engineering workflows. This fully online course will tackle this challenge by introducing a big picture map of the field and by providing an intuitive understanding of how AI works.

Updated on
Fakta
Study point

5

Level

Bachelor

Method of work

Online

Teaching language

English

Neste startup

Fall 2024

Application deadline

01.09.2024

Price

Semester fee

Content

The course provides a comprehensive introduction to the fundamental concepts and mathematical principles underpinning artificial intelligence (AI) and machine learning (ML). Through a series of engaging lectures and hands-on programming exercises, students will explore topics ranging from linear algebra and dimensionality reduction to machine learning techniques, neural networks, and natural language processing (NLP).

To voksne personer ser på en gjennomsiktig skjerm i et industrielt rom

The course is designed for individuals interested in pursuing careers in data science, AI engineering or related fields, and assumes basic proficiency in programming and mathematics.

Upon successful completion of the course, you will gain the confidence in how to start scoping, planning, and considering AI tools effectively into your workplace to increase productivity and decrease repetitive tasks.

Knowledge

  • A deep understanding of the math that makes machine learning algorithms work.
  • Able to explain fundamental machine learning concepts and algorithms, and their implementation.
  • Differentiate between supervised and unsupervised learning techniques and select appropriate algorithms for different scenarios.
  • Employ appropriate evaluation metrics to assess the performance of the models.
  • Understand the strengths and limitations of well-known machine learning methods and learn how to analyze data to identify trends.

Skills

  • Implement machine learning algorithms and neural networks using programming languages such as Python and libraries like NumPy, TensorFlow, and Keras.
  • Build language models and understand their applications in natural language processing tasks.
  • Solve real-world problems through hands-on use cases and programming exercises, reinforcing theoretical concepts with practical experience.
  • It is a fully web-based course. All the lectures are published as pre-recorded videos at once and students have immediate access to the entire course content.
  • Optional laboratory sessions will be scheduled.

3 hour digital school exam (NB! physical exam in Stavanger)

Date: to be decided

Requirements for taking the exam

  • Mandatory assignment
  • Students are required to complete an individual compulsory programming assignment (Approved/Not approved), which must be passed to qualify for the written exam. The assessment of the assignment consists of a report and an oral presentation.
  • The course work requirement is only valid for a period of two years.
General admission requirement:

Krav om generell studiekompetanse + HING

Du må dokumentere Matematikk R1 (eller Matematikk S1 og S2) og R2 og Fysikk 1.

Du dekker kravet, selv om du ikke har generell studiekompetanse, hvis du har:

  • bestått 1-årig forkurs for 3-årig ingeniørutdanning og integrert masterstudium i teknologiske fag etter fagplan av 2014 eller
  • bestått 1-årig forkurs for ingeniør- og maritim høyskoleutdanning eller
  • bestått 2-årig teknisk fagskole (rammeplan fra 1998/99 eller tidligere ordninger).

Recommended prerequisite knowledge:

DAT120 Introduction to programming, MAT100 Mathematical methods 1, MAT200 Mathematical methods 2, STA100 Probability and Statistics 1

Applicants with a foreign education 

You must document your higher education entrance qualification. You can find more information about this here. The language requirement is mandotary for English and Norwegian.

You must upload an offical translated diploma in either English or a Scandinavian language before submission.

Language requirements

Applicants with Norwegian or English as a second language must document sufficient knowledge of Norwegian or English.

To learn more about the language requirement go to Samordnaopptak: https://www.samordnaopptak.no/info/utenlandsk_utdanning

You can read more about the approval of foreign educationon the website of Direktoratet for høyere utdanning og kompetanse: https://hkdir.no/utdanning-fra-utlandet

If you do not meet the language requirements above you may apply on the basis of prior learning. If the default language at work is english, please upload a document from your manager/HR manager that confirms your language proficiency.

The literature will be available in Leganto.

Lecturer

Associate Professor
51834507
Faculty of Science and Technology
Department of Electrical Engineering and Computer Science

For questions about the course , please contact the adminsitration of Faculty of Science and Technology e-mail: post-tn@uis.no/ number: 51831700.