2018-2 Artificial Intelligence

Class Info:

    • Time: Class1 (23550): Tue 10:00-13:00, Class2 (23551): Thr 13:00-16:00
    • Location: Cluster Bd (학연산클러스터), Room 509

Textbook: Python Machine Learning, 2nd Ed, Raschka & Mirjalili, PACKT Publishing

    • The first edition may work, but beware that it contains many typos and errors.

Grading: (Attendance, Team-projects, Midterm, Final) = (10%, 50%, 20%, 20%)

Links


Exams

    • Midterm
      • Date/time: Oct 12 (Friday), 6 ~ 8pm
      • Location: Conference Hall, room 104 (campus map)
      • Important Topics [link]
    • Final
      • PBL-3 Report is due:
        • Class 1: Dec 18 by 13:00
        • Class 2: Dec 20 by 16:00
        • Email to TA: nomar0107@gmail.com
          • Subject format (이메일 제목 양식):
            • Class 1(Tues): [PBL3-1] your student ID number
            • Class 2(Thurs): [PBL3-2] your student ID number
          • Send your Jupyter Notebook (pbl3-yourstudentid.ipynb) file as an attachment
        • YOU MUST FOLLOW THE ABOVE SUBMISSION FORMAT (위 제출 양식을 반드시 지켜야 합니다).
        • NO LATE SUBMISSION WILL BE ACCEPTED (제출 시한을 넘겨서 도착한 이메일은 0점 처리합니다)


Schedule

    • Week 1: Introduction to AI [note]
    • Week 2: Basic methods in ML (Logistic regression, Neural Networks, SVM) [note1, note2]
    • Week 3~5 (PBL Case Study #1) (How to read MNIST files)
      • Team composition(class1 , class2), Problem understanding, Per team discussion & solution making
      • Team presentations & feedbacks (send presentation files to nomar0107@gmail.com)
      • Cross validation & Grid Search [note]
      • Team presentations with improvements, Team / Member evaluations
    • Week 6: Midterm (Oct 12)
    • Week 7: Gradient-based Learning
    • Week 8~10 (PBL Case Study #2)
      • Team composition(class1, class2), Problem understanding, Per team discussion & solution making
      • Team presentations & feedbacks (class 1: Nov. 6, class 2: Nov. 8)
        • Your team needs to a short presentation (send slides to TA)
        • Content:
          • Understanding of SVM using SGD
          • Your strategy for coding:
            • Show your class body (sketch)
            • If you're going to use GridSearchCV
            • which hyperparams to tune
            • feature transformation?
            • who's going to do what...
        • SGD Algorithm for solving SVM [note]
      • Code submission 1 (class 1: by Nov 12, class 2: by Nov 14)
        • TA : nomar0107@gmail.com
        • Python code: team#.py <training_data> <test data>
        • Specify what is your training data.
        • Fix hyperparameters in your code.
        • Output labels per line, as explained in PBL 2 problem description.
      • Unsupervised Learning (PCA)
      • Additional MNIST data
        • New 1k [images, labels]
        • D1+D2+New 1k data [images, labels]
          • We will use this as the new <training_data> for the next evaluation
      • Code submission 2 (class 1: by Nov 19, class 2: by Nov 21)
      • Final resentation (class 1: Nov 13, class 2: Nov 15)
        • Collect all contents from your previous talks: you may only briefly explain them during the final talk.
        • Details about feature extraction
        • Details of hyperparameters chosen, with plots representing the difference of accuracy with respect to the hyperparameters
        • Contribution of team members: who did what?
      • Team presentations with improvements, Team / Member evaluations
    • Week 12~14 (PBL Case Study #3)
    • Week 15: Activity report, Final Exam (Report)