NDAB20002U Artificial Intelligence (AI)
BSc Programme in Machine Learning and Data Science
Introduction to models and methods used in artificial intelligence (AI), and machine learning methods relevant to AI. Different aspect of AI are covered including symbolic AI, representation learning, advanced deep models, and reinforcement learning.
general aspects of AI, weak vs. strong AI
history of AI
aspects of symbolic AI (knowledge bases, logic, reasoning)
advanced deep models (recurrent networks, graph networks)
variational Bayesian methods (variational autoencoders)
select appropriate methodology to solve AI problems
implement selected AI algorithms
design and train representation learning algorithms
design and train reinforcement learning algorithms
reflect upon the capabilities and limitations of AI algorithms
recognising and describing possible applications of AI methodology
design, optimise and use advanced deep models
apply the learned methodology to applications in analysis of real-world data such as images, sounds and text
rigorously analyse AI learning algorithms
See Absalon for course litterature.
1. Linear algebra corresponding to the course Lineær Algebra i datalogi (LinAlgDat).
2. Calculus corresponding to the courses Introduktion til matematik i naturvidenskab (MatIntroNat) and Matematisk Analyse (MatAn).
3. Basic statistics and probability theory corresponding to the course Sandsynlighedsregning og statistik (SS), and statistical modelling corresponding to the course Modeller for Komplekse Systemer (ModKomp).
4. Machine learning corresponding to Introduktion til Machine Learning (MaLeIntro). Please note that MaLeIntro includes basic deep learning.
5. Programming experience in Python.
- 7,5 ECTS
- Type of assessment
- Continuous assessmentContinuous assessment of 4 written assignments (2 to be completed individually, and 2 to be completed in groups). All assignments must be passed. The final grade is based on an overall assessment.
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
Several internal examiners
The re-exam is 25 minutes oral examination, without preparation, in full course syllabus.
Criteria for exam assesment
According to learning outcomes.