Artificial Intelligence for Engineers DAT305

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

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.

Recommended prerequisite knowledge:

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

General admission requirements:

Bachelors equation in engineering.

Read more about the admission requirements here: Universitet og høgskole - Samordna opptak. If you apply on the basis of formal competence , the necessary documentation must be uploaded at the same time as you apply.

Admission based on prior learning (realkompetanse)

If you wish to apply for admission to higher education, are aged 25 or over and do not have higher education entrance qualifications, you may apply on the basis of prior learning. The University of Stavanger itself has the authority to assess what qualifications that are required. Please upload a CV and work-certificate.

Applicants with a foreign education 

You must document their higher education entrance qualification according to the GSU-list. You can find more information about the GSU-list here: https://www.nokut.no/en/foreign-education/GSU-list/ by choosing the country where your education is taken. 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 also go to NOKUT to see which countries require an English test (GSU list) GSU-listen | Nokut.

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.