Applied Artificial Intelligence (Summer University) (Spring 2023)
Official course description:
Course info
Programme
Staff
Course semester
Exam
Abstract
In this course, the students learn how to evaluate, design and prototype products based on latest artificial intelligence technologies.Description
Artificial intelligence driven technology is becoming a central part of an increasing number of products, ranging from self-driving cars, to home assistants. Being able to design products based on this technology and evaluate their impact is a complex skill that requires knowledge if the inner working of different artificial intelligence algorithms and the ability to experiment with them.
While maintaining a product centric perspective, this course aims at giving the students the ability to understand the different mechanisms at the basis of modern artificial intelligence and use this knowledge to design and evaluate AI based applications.
NB: Please not that you can't take the course if you have already taken Introduction to Artificial Intelligence.
Formal prerequisites
- understanding of basic computing concepts -- e.g. what is a process, what is a file or what is memory
- understanding of fundamental principles of imperative programming -- e.g. variables, functions, parameters
- experience in writing, building and running simple applications written in a scripting language -- e.g. python, processing, javascript
- understanding basics of linear algebra and calculus (matrices, vectors and derivatives)
Intended learning outcomes
After the course, the student should be able to:
- formulate ideas on how to include artificial intelligence technologies in new products
- analyse the impact of AI on the potential end-user and discuss ethical aspects of AI products.
- describe the mechanisms that drive modern day artificial intelligence
- compare multiple algorithms and identify the most appropriate for a given application
- identify advantages and limitations of artificial intelligence algorithms
- compile simple prototypes based on pre-existing library to experiment with AI methods
Learning activities
The course consists of exercises integrated into lectures with the teacher. The purpose of the lectures is to provide the context of the topics covered and available tools. During the lectures, the students will immediately get practical experience applying the tools to problems relevant to their exam hand-ins. There is a strong emphasis on problems and practices a student might come across in their professional environment.
Topics covered:
Supervised and unsupervised learning, neural networks, ensemble methods and optimization.
Course literature
The course literature is published in the course page in LearnIT.
Ordinary exam
Exam type:C: Submission of written work, External (7-point scale)
Exam variation:
C11: Submission of written work
To get a grade in this course you need to complete notebooks assigned in each lecture. We have 9 lectures so you will get one or a few notebooks in each lecture.
All notebooks must be submitted and approved. In the notebooks, you must answer all the questions and tasks to show that you completed the learning outcomes. We tolerate minor mistakes, especially in open-ended questions. Collaboration on notebooks is not allowed.
reexam
Exam type:C: Submission of written work, External (7-point scale)
Exam variation:
C11: Submission of written work
To get a grade in this course you need to complete notebooks assigned in each lecture. We have 9 lectures so you will get one or a few notebooks in each lecture.
All notebooks must be submitted and approved. In the notebooks, you must answer all the questions and tasks to show that you completed the learning outcomes. We tolerate minor mistakes, especially in open-ended questions. Collaboration on notebooks is not allowed.
Time and date
Ordinary Exam - submission Fri, 11 Aug 2023, 08:00 - 14:00Reexam - submission Fri, 1 Sept 2023, 08:00 - 14:00