About the instructor

Course logistics

  • Course objective: Pragmatic data mining on real data
  • Work for the course
    • Class meets every Friday 15:00-18:00
    • Most classes will be consisted of two parts
      1. Lectures and/or tutorials
      2. Short presentations by students
  • Course website: http://lucypark.kr/courses/2015-ba
    • Lecture notes
    • Schedule/Announcements
  • Schedule: http://lucypark.kr/courses/2015-ba/#schedule
  • Office hours
    • Right before every class
    • You are welcome to ask any kind of questions
    • You are also encouraged to book ahead, or your meeting may have to be deferred to another time
  • Grading
    • Assignments (20%): Two graded assignments related to your project, and two reading assignments during the semester
    • Mid-term exam (20%): In-class exam covering the first half of the semester
    • Finals (60%): Finals consist of an in-class exam and a project presentation

Term Project

  • You will be conducting a term project throughout the semester
  • Term projects will be done as individual work
  • There will be two designated assigments according to the project, and one graded presentation at the end of the semester
  • Extra credit will be given to those who submit and/or rank in public tournaments (ex: Kaggle)

Reading assignments

Advice on your projects

  1. The best topics are the topics you are actually interested in
    • You should be able to "dogfood" your own analysis
  2. Don't be afraid to shift the project's direction
    • However, shifting too much will give you less time for real work -- balance!
  3. Feel free to use project results in your graduation project or paper
    • Grab two rabbits at once!
    • These projects have potential to become something in your portfolio
    • May be a plus when you get a job, or apply for grad school

Presenting project progress

  • Update your progress (Max. 10 minutes)
    1. Prepare printouts (for the whole class) or slides or whatever format that best conveys your work
      • Please share your materials at the forum before class
    2. Be brief yet clear
    3. Take notes
      • Keep precise research progress and feedback notes
  • A good presentation contains the following:
    1. What questions you had
    2. What approach you chose to alleviate such questions
    3. What results you achieved
    4. What questions you further got and what you plan to do next
    5. (Optional) Tricks and tips you want to share with the class

Asking questions

Never hesitate in asking questions

  • Private questions: [email protected]
    • Personal questions and/or requests
    • Assignment submissions that regard privacy
  • Public questions: [email protected]
    • This is the class forum
    • Everything else you want to ask goes here
    • Using any language of your choice (ex: English, Korean, Java, ...)
    • Asking good questions at the class forum
      • Provide as much details as you can
      • However, be "brief" and "clear"
      • In case of programming questions, explicitly list versions of software being used (including packages and OSs)

Academic integrity

Academic integrity is the moral code or ethical policy of academia. There may be times you are tempted to be dishonest, cheat, or plagarize other work, but in this course (and undoubtfully in all other classes), we encourage you to approach your work with honesty and integrity.

  • What is disallowed
    • Don't ask another student to do the work for you
    • Don't fabricate experimental results
    • Don't cheat on exams, and don't let anothers copy your answers
  • What is allowed
    • Do trust your ability
    • Do give credit to others' work (Mind your citations!)
    • Do brag about your acheivements