Credits: 5

Schedule: 18.09.2019 - 11.12.2019

Contact information for the course (applies in this implementation): 

Jorma Laaksonen (jorma.laaksonen@aalto.fi)

Teaching Period (valid 01.08.2018-31.07.2020): 

varies

Learning Outcomes (valid 01.08.2018-31.07.2020): 

After the course, the student knows how to carry out a scientific project and write a scientific report in the field of computer and information science.

Content (valid 01.08.2018-31.07.2020): 

A project work, which can be done in a group, from the field of computer and information science.

Details on the course content (applies in this implementation): 

The goal of the course is to give students an opportunity to work on a small research project within the field of machine learning and data science. During the course, the students will focus on a research topic under the supervision of a researcher. They will present their progress in order to get feedback and will practice their presentation skills. At the end of the semester, a final report in the format of a research paper will be submitted.

Assessment Methods and Criteria (valid 01.08.2018-31.07.2020): 

Report and presentation. The course grade is determined by the report (100%).

Elaboration of the evaluation criteria and methods, and acquainting students with the evaluation (applies in this implementation): 


optional course assignments

  • 5-minute midpoint presentation
  • 15-minute final presentation

compulsory course assignments
  • A final report of 5-10 pages
The final grade reflects the performance of the student, based on the interaction with the supervisor, and the quality of the report. Grade 5 is max, and 1 is the minimum passing grade. In general, we try to avoid inflation, and we propose the following target distribution,

Grade  Quality  Target % of students in that bin
5  excellent, publishable-level  20%
4  good, solid working   40%
3  just met expectations   25%
2  below expectations
  10%
1  bad      5%

Workload (valid 01.08.2018-31.07.2020): 

Seminars. Independent or group work (discussions with supervisor, programming, reporting, preparation of presentation).

Details on calculating the workload (applies in this implementation): 


The number of credit units reflect the effort in working hours. The number of credit units for the course is between 5 and 10. As a rule of thumb, 1 credit unit corresponds to 27 hours of work. Note that “full” courses are typically 5 credit units.

Substitutes for Courses (valid 01.08.2018-31.07.2020): 

T-61.5910 Research Project in Computer and Information Science

Grading Scale (valid 01.08.2018-31.07.2020): 

0-5

Details on the schedule (applies in this implementation): 

  • Sep 18, Wed, 14.15-16.00, TU3 : Course logistics and presentation of the research topics. All students registered in the course can attend to know more about the course.
  • Oct 30,  Wed, 14.15-16.00, TU3 : Midpoint presentations.
  • Dec 11, Wed, 14.15-18.00, T5 : Final presentations.

Description

Registration and further information