Machine Learning for Artificial Intelligence (A24)

Code

IT-MAL2

Version

1.0

Offered by

ICT Engineering

ECTS

Prerequisites

The course requires basic programming skills and the participants should be comfortable writing non-trivial code in Python. Additionally, basic knowledge about data science is advantageous, but not required.

Main purpose

"Machine Learning for Artificial Intelligence" is a course that explores the fundamental concepts, techniques, and applications of deep learning in the context of artificial intelligence (AI). This course is designed to provide students with a comprehensive understanding of how deep learning methods can be leveraged to solve complex AI problems.

Knowledge

After having completed the course, the student will have gained knowledge about algorithms, methods, techniques, tools, and applications within the following:

- Different types of neural networks, e.g. feed-forward, convolutional and recurrent neural networks.
- Predictive methods, e.g., image classification and speech recognition.
- Generative methods, e.g., generative adversarial networks (GANs) and generative pre-trained transformers (GPTs).
- Reinforcement methods, e.g., game AI.

Skills

Upon completion of this course, students should be able to:

- Understand and apply a range of deep learning methods for AI.
- Implement and fine-tune deep learning models in a programming language.
- Apply ethical considerations when developing AI systems.

Competences

Upon completion of this course, the goal is that the students have acquired the competences to:

- Make informed choices about the use of deep learning methods.
- Communicate and discuss the theory, tools and techniques of deep learning and artificial intelligence.
- Discuss, address and reflect upon ethical aspects of using artificial intelligence.

Topics

Teaching methods and study activities

The mode of teaching will be classroom based and will involve lectures by the teacher and exercises/assignments made in class. The students are expected to participate actively in the lectures and to work on the course between classes.

During the semester, the students should solve a number of assignments. These will make up their portfolio. In addition to this, the course concludes with a larger group project. These will form the basis of the exam.

The total work-load for the student is expected to be around 125 hours.

Resources

Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow by Aurélien Géron. 3rd edition, 2022.

Additional material will be uploaded to Itslearning.

Evaluation

Examination

Exam prerequisites:
None.

Exam type:
The course is evaluated based on an oral examination, which will take 20 minutes including everything.
At the exam, the student will randomly draw one of the portfolio assignments. The exam will then take place as a discussion of this assignment, the students’ group project and the curriculum in general.

Internal assessment.

Tools allowed:
The student is expected to bring their portfolio assignments and their final project to the oral exam, such that they are able to display and run their code.

Re-exam:
The re-exam is the same as the ordinary exam.

Grading criteria

Grading based on the Danish 7-point scale.

Additional information

Responsible

Frederik Thorning Bjørn (frbj)

Valid from

2/1/2024 12:00:00 AM

Course type

6. semester
7. semester
Elective for the specialization Data Engineering
Electives
Web 6 og 7

Keywords

Artificial intelligence. Deep learning. Neural networks. Image classification. Speech recognition. Natural language processing. Reinforcement learning.