2017/1
Department of Computing
Students attending the class are expected to have basic knowledge in topics covering probability, linear algebra, statistics and algorithms.
The requirements of this course consist of participating in lectures, 2 programming assignments, midterm, final examination and a project. The aiming of this course is that the students understand the basic concepts of machine learning and be able to use the presented techniques to solve real problems. The grading breakdown is the following:
The grading breakdown for the components of the final project is the following:
Homeworks and exams may contain material that has been covered by papers and webpages. Since this is a graduate class, we expect students to want to learn and not google for answers. You should cite the materials you used.
Homeworks will be done individually: each student must hand in their own answers. It is acceptable, however, for students to collaborate in figuring out answers and helping each other solve the problems. We will be assuming that, as participants in a graduate course, you will be taking the responsibility to make sure you personally understand the solution to any work arising from such collaboration. You also must indicate on each homework with whom you collaborated.
You will be allowed 2 total late days without penalty for the entire semester. You may be late by 1 day on two different homeworks or late by 2 days on one homework. Weekends and holidays are also counted as late days. Late submissions are automatically considered as using late days.
Once those days are used, you will be penalized according to the following policy: