The course covers selected topics in machine learning and computing in general. Topics include text similarity measures, location-aware search engine, web mining and density estimation. Other topics will be selected during the course based on what is going on in the research in the machine learning group. The course is suitable to MSc and PhD students who wish to gain deepen their knowledge in machine learning.
Course consists of 10-12 seminar presentations, of which half is given by the teacher, and half by the students. Participants will also serve as opponents to each others topic focusing on one of the three aspects: (1) slide content, (2) the presenter, (3) the scientific content.
The basic variant of the course (3 cp) will be graded as accept/fail. Extended variant includes small project work, and will be graded using normal 1-5 grading system.
Teacher: Pasi Fränti
Schedule: 22 h, starting from 17.1.
Thursday 14-16 (either 178 or 180; see WebOodi)
Lectures every 2-3 weeks. Exact times will be announced.
Schedule and Lecture Notes
Design and Analysis of Algorithms (must be passed)
Grading: Passed/Failed
Passing the course will require:
- Participation in the lectures (10 times minimum)
- Giving seminar presentation
- Being opponent to other presentations
Content reviewer:
Was the introduction / body / conclusion of the presentation convincing?
Did the presentation title correspond to the content of the presentation?
Who were the contributors to the presentation?
Was the content correct for the target audience and was it useful?
Was the audience able to keep up with the content and was it clear?
Were the main ideas of the presentation clear?
Design reviewer (slides and visuals):
Were the slides clear and easy to follow?
Did the slides contain too much or too little information?
Were the colors and sizes of the font and visual (pictures, graphs) adequate?
Were the main points of the presentation properly emphesized?
Did the slides help the credibility of the presenter?
Did the slide design catch the attention of the audience?
Performance reviewer:
Was the speech clear and well paced?
Was the presenter convincing?
Did the presenter have good eye contact, posture and gestures?
Was the presenter able to keep the attention of the audience?
How the presenter managed the equipment?
Q&A reviewer:
How prepared was the presenter for the Q&A?
Did the presenter hadle the questions well?
Did the presenter answer correctly to the question?
Material will be delivered during the course.