Example script for manually adding courses on hkn-rails

manual_add_courses.rb

cs_dept = Department.where(abbr: "COMPSCI").first()
info_dept = Department.create(
    name: "Information",
    abbr: "INFO",
)

# Create CS 194-26 and CS 294-26
# For course information, see https://inst.eecs.berkeley.edu/~cs194-26/fa22/
for course_number in [194, 294] do
    Course.create(
        suffix: "-26",
        name: "Intro to Computer Vision and Computational Photography",
        description: "The aim of this advanced undergraduate course is to introduce students to computing with visual data (images and video). We will cover acquisition, representation, and manipulation of visual information from digital photographs (image processing), image analysis and visual understanding (computer vision), and image synthesis (computational photography). Key algorithms will be presented, ranging from classical (e.g. Gaussian and Laplacian Pyramids) to contemporary (e.g. ConvNets, GANs), with an emphasis on using these techniques to build practical systems. This hands-on emphasis will be reflected in the programming assignments, in which students will have the opportunity to acquire their own images and develop, largely from scratch, the image analysis and synthesis tools for solving applications.",
        units: 4,
        prereqs: "This is a heavily project-oriented class, therefore good programming proficiency (at least CS61B) is absolutely essential. Moreover, familiarity with linear algebra (MATH 54 or EE16A/B or Gilbert Strang's online class) and calculus are vital. Experience with neural networks (e.g. CS182 or equivalent) is strongly recommended. Due to the open-endedness of this course, creativity is a class requirement.",
        department_id: cs_dept.id,
        course_number: course_number,
    )
end

# Create Info 290T - Computer Vision
# For course information, see https://www.ischool.berkeley.edu/courses/info/290t/cv
Course.create(
    suffix: "T",
    name: "Computer Vision",
    description: "This course introduces the theoretical and practical aspects of computer vision, covering both classical and state of the art deep-learning based approaches. This course covers everything from the basics of the image formation process in digital cameras and biological systems, through a mathematical and practical treatment of basic image processing, space/frequency representations, classical computer vision techniques for making 3D measurements from images, and modern deep-learning based techniques for image classification and recognition.",
    units: 3,
    prereqs: "Linear algebra and Python (INFO 206A & B or equivalent).",
    department_id: info_dept.id,
    course_number: 290,
)