Frequently Asked Questions

Thank you for registering for the 2023 Imaging Science Summer Math Crash Course. MCC is less than a month away and we are looking forward to meeting you all! Here are some frequently asked questions regarding the course logistics/registration, etc. 

  • Do I need to register formally (on webstac) in order to take this course? 
    • No, there is no formal registration required (outside of the google form). You do not get any credits for taking this course, and your work will not be graded! 
  • Will there be recorded sessions? I might not be able to join sessions during the day due to work/other courses/time zone etc.  
    • We plan on scheduling two tutoring sessions for each topic (for example, one session in the morning and one session in the evening, central time, in person and or zoom) to accommodate students across different time zones. Each week tutors will hold a total of four sessions, covering materials that are related to the course materials. Recordings are contingent, as we plan to ask tutors and the students each session for consent to record and share. 
  •  The pre-assessments were too hard for me. Should I still attend the crash course?
    • The pre-assessments are mainly targeted for Imaging Science Students, as the required core courses build on those concepts. If you had a hard time with the pre-assessment but wanted to learn about the topic, we highly encourage you to participate in the crash course.
  • I will be fully remote this summer, can I still be involved? 
    • Yes! We plan to have hybrid (in-person with zoom option to participate, or depending on the tutor and schedule only on zoom) sessions. This means that you will be able to participate fully remotely and all tutoring sessions will be accessible via Zoom. 
  • What is the course going to be like? What materials, classes, resources will be available? 
    • We really want to emphasize that this is an independent study based course where we provide guided materials alongside help sessions with tutors. Although we don’t provide a traditional ‘lecture’ style course, we curated a list of materials you can work on at your own pace. We will also have 2 help sessions each week (same session will be provided at two different times, so a total of 4 sessions) that you can attend to ask questions and interact with fellow students. We also curated GoogleColab Notebooks for certain modules (PCA, SVM, etc.). 
  • I don’t have time to follow the course timeline and join the problem sessions, what should I do?
    • We designed these courses as a self-study curriculum. While we have made an effort to curate the textbooks, modules, exercises and provide structured ways to engage with them, how you use these resources is completely your decision! The timelines and content pacing reflect our best guess of what is helpful to you. If you need additional help and cannot join the problem questions, feel free to ask questions through our canvas and discord. 
  • How do I get access to the textbooks?
    • If you have trouble finding any of the resources, please reach out to your tutors. We are happy to point you towards the used textbook provided resources.

  • Some more details on the course contents
    • Calculus with application in probability provides an introduction to fundamental Calculus concepts (review of trigonometry, differentiation, and integration). The application in probability focuses on set notation, random variables, and probability distributions. [Link to course outline]
    • LA with application in Machine Learning will cover an introduction to linear algebra (systems of linear equations, matrix operation, and vector spaces) as well as fundamental linear algebra concepts (eigenvectors, eigenvalues, singular value decomposition, orthogonality, and hyperplanes) that will allow you to understand Principal Component Analysis and Support Vector Machines (SVM). You will learn how to build your own PCA and SVM algorithm using Python and Google Colab. [Link to course outline]
    • Signals & Systems will provide you with an introduction to sampling theory, convolution, and Fourier transform.