GRA 4150 Artificial Intelligence - Technologies and Applications
The main course objectives are to demystify AI, learn AI methods, learn how AI is put into practice in businesses, and learn to assess the potential benefits/harms of AI.
After finishing the course the candidate should know:
- central AI methods
- how AI applications are used in various industries
- ethical and practical consideration for the use of AI
After finishing the course the candidate should be able to:
- program basic AI algorithms in Python
After finishing the course the candidate should:
- feel more confident when collaborating with AI specialists
- be able to assess how the use of AI could give a competitive advantage
- be able to read and understand AI literature on your own
- Overview of AI: definitions and history
- AI applications: computer vision, NLP, economics/finance
- Machine Learning-based methods: artificial neural networks
- The future of AI
The course will also provide many examples of AI uses and the reality of AI technologies today, including:
- Computer vision
- Natural language processing
- One or two other topics TBD.
We will also discuss practical considerations for doing an AI project.
As this course is an elective, the teaching and learning activities are tentative and subject to adjustments based on class size.
The course will be a combination of:
- Lectures / Recorded lectures / Guest lectures
- External videos
- Articles/blogs/tutorials on specific topics
Familiarity with python is expected, and some familiarity using machine learning packages (scikit-learn, XGBoost, pytorch, tensorflow) is an advantage.
Please note that while attendance is not compulsory in all courses, it is the student’s own responsibility to obtain any information provided in class.
All courses in the Masters programme will assume that students have fulfilled the admission requirements for the programme. In addition, courses in second, third and/or fourth semester can have specific prerequisites and will assume that students have followed normal study progression. For double degree and exchange students, please note that equivalent courses are accepted.
Deviations in teaching and exams may occur if external conditions or unforeseen events call for this.
- Linear regression analysis, at the level of GRA 4110 Applied Data Analytics
- Python, at the level of GRA 4142 Data Management and Python Programming
- Data modeling, at the level of GRA 4142 Data Management and Python Programming
Form of assessment:
Examination when next scheduled course
|Form of assessment:
|Examination when next scheduled course
Student's own work with learning resources
Group work / Assignments
A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 6 ECTS credits corresponds to a workload of at least 160 hours.