Latest Advances in Artificial Intelligence

Course Objective

The objective of the course is to allow students to learn about and acquire knowledge in state-of-the-art AI and Machine Learning Methods.

1. Knowledge and understanding

• Students will learn about the most recent state-of-the-art machine learning techniques and AI methodologies.
• Students will understand current developments and future trends in both application and research in AI

2. Applying knowledge and understanding

• Students develop the skills and an overview necessary to understand and interpret recent developments in AI.

3. Making judgments

• Students will be able to critically discuss recent publications, select application-relevant methods and connect them to theory.

4. Communication skills

• Students can communicate the advantages and technical details of state-of-the-art Machine Learning and AI methods to their peers and other experts.

5. Learning skills

• Students will improve their ability to read and reflect on (some) state-of-the-art papers.

Course Content

This course is a Seminar discussing the latest advances in AI. The course will focus on advanced topics in AI and machine learning using a mixture of direct lectures and seminar-style sessions. Students will be active participants in this course and develop a broader understanding of the latest advances in AI.

Before the start of the seminar series, students can select one of multiple tracks discussing a specific topic in depth throughout the course. Possible topics include Robot Learning, Commonsense Reasoning, Ethics, Collective Intelligence, Large Language Models Security, and Neural Network Architectures.

By the end of the seminar, the student will have developed in-depth knowledge of a selected area of AI and its wider context.

Additional Information Teaching Methods

The course format is a combination of lectures, seminars and assignments. The form of assignment will be decided with the leading lecturer of the seminar track the student has selected.

Method of Assessment

Students will be assessed on both an individual assessment examination and a non-individual assessment grade (eg group projects, class actvities) . The individual assessment is weighted at 45%, the non-individual assessment grade (eg group projects, class actvities) contributes another 45% towards the final grade. Examination and grading of these components depends on the selected track in the seminar but will be communicated at the start of the course. Attendance and active participation are required and make up 10% of the final grade for this course.

A resit is not offered.

Literature

Literature will be provided throughout the course.

Additional Information Target Audience

MSc Artificial Intelligence

Additional Information

Please, note that this course requires you to participate in-person for most seminar activities and has an attendance requirement (see above).

Use of generative AI during this course is permitted in a limited way, with disclosure

In this course, generative AI (GenAI), such as ChatGPT or similar tools, may be used in a limited way, for example for editorial support, rephrasing, or structure. Substantive contributions from GenAI may only be used within the limits set by the lecturer. Any use of GenAI must always be explicitly disclosed, in accordance with the lecturer's instructions. The student remains fully responsible for the content, quality, and accuracy of the submitted work. Incorrect use of GenAI is considered fraud. If established, the submitted work or assessment result will be declared invalid, and the Examination Board may impose further measures.

Explanation Canvas

The course will use Canvas. Further important registration information such as signup to groups will be provided before the first lecture week. Hence, it is recommended to sign up early and monitor the Canvas page and/or emails for important course information.