AI 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.
Course description for study year 2024-2025. Please note that changes may occur.
Course code
DAT305
Version
1
Credits (ECTS)
5
Semester tution start
Autumn
Number of semesters
1
Exam semester
Autumn
Language of instruction
English
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).
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.
Learning outcome
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.
Required prerequisite knowledge
Recommended prerequisites
Exam
| Form of assessment | Weight | Duration | Marks | Aid |
|---|---|---|---|---|
| Written exam | 1/1 | 3 Hours | Letter grades | None permitted |
Digital school exam.
Coursework requirements
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.
Course teacher(s)
Course coordinator:
Mina FarmanbarHead of Department:
Tom RyenMethod of work
- 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.