Seminar in Machine Learning and Artificial Intelligence

BSc/MSc - Autumn/Spring Semester
102469, Lectures and exercises, 5.0 ECTS

Lecturer Prof. Dr. Paolo Favaro
Location Neubr├╝ckstrasse 10 (N10), room 302
Time Mondays (Spring), Tuesdays (Autumn), 10:15 to 12:00
ILIAS KSL

Introduction

This course aims at providing an overview and understanding of the state of the art research in computer vision and machine learning. Moreover, it will allow students to develop soft skills such as: critical analysis, presenting, and team work. Recent publications are discussed in detail so that students can build a deep understanding of the methodologies and concepts in these fields. This is a continuous evaluation course. Thus, every discussion and presentation of the students is used in their assessment. Some sessions may be substituted by seminars.

Reading material

The reading material is selected from relevant papers of computer vision and machine learning conferences and journals. The seminar also provides the opportunity for students pursuing Bachelors and Masters projects to present their work.

Learning outcome

Upon successful completion of the course the student 

  1. will be able to read and critically analyze scientific publications in computer vision 
  2. wil have developed presentation skills and the ability to discuss and answer questions 
  3. will be able to work in teams or independently and manage his/her time 
  4. will have analysed and discussed work in computer vision/machine learning

Schedule of work one week prior to the seminar

  1. A research paper in computer vision or machine learning is selected a week in advance and posted in ILIAS.
  2. Students read the paper ahead of the seminar

Schedule of work during the seminar

  1. A questionnaire is distributed and students split in groups (random allocation).
  2. Groups discuss and prepare answers [~1h20m].
  3. Each groups presents the answers [~20m].
  4. Assessment of the presentation of each group is based on: correctness, complexity, depth, clarity of presentation.
  5. Grade will be the average of the above continuous assessment.

Some seminars might be allocated to guest lecturers or selected topics.