FAQ
I am unable to locate this course on Brightspace. Could you provide assistance?
The course materials are accessible via the dedicated website turcotte.xyz/teaching/csi-5180. Since Brightspaceis not particularly effective to support discussion groups and assignment submissions, these functions will be managed through Microsoft Teams.
Is it permissible to enroll in both CSI 5180 W00 and CSI 5180 X00 concurrently and receive academic credit for each?
Certainly, students can enroll in both CSI 5180 W00 and CSI 5180 X00 concurrently and receive credit for each. CSI 5180 is categorized as a “topics course,” a flexible course code that allows faculty to introduce new subjects without the lengthy process of developing a formal course, which requires Senate approval and can take up to 18 months. This approach enables instructors to gauge student interest and demand for a subject over a period of 2 to 3 years. Importantly, the sections W00 and X00 are distinct from one another, permitting students to earn credits for both.
What level of biological knowledge is required for this course?
Understanding biology is crucial since bioinformatics aims to address real-world problems. To ensure everyone is on the same page, we will dedicate at least two lectures to cover essential concepts of molecular biology of the cell. Moreover, we will continuously revisit these concepts throughout the course as new problems are introduced. At a minimum, you should have a keen interest in learning more about biology.
Is previous experience in bioinformatics necessary?
No prior experience in bioinformatics is required. I have been teaching a course titled Algorithms in Bioinformatics (CSI 5126) for several years, which focuses on the data structures and algorithms fundamental to bioinformatics applications. However, in this course, we will pivot towards using machine learning approaches rather than traditional algorithmic methods, so no background in bioinformatics is needed.
What foundational knowledge is expected for this course?
To make this course comprehensive and self-sufficient, I do not assume any prior knowledge of machine learning. Nonetheless, a basic grasp of probability and statistics, along with calculus and linear algebra, is essential. You are also expected to be proficient in programming with a high-level language, particularly Python.