2019-2 Artificial Intelligence

Class Info:

    • Time: Class1 (24610): Tue 9:00-12:00, Class2 (23504): Thr 9:00-12:00
    • Location: Cluster Bd (학연산클러스터), Room 506

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

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

Links


Exams

    • Midterm
      • Oct 23, 7~8:30 pm
      • Location: Cluster Bd.
        • Room 506: Tuesday class
        • Room 507: Thurs class
        • There will be no lecture on Tue/Thurs in this week (시험이 있는 주에는 수업이 없습니다).
      • Important Topics 2019 [link]
    • Final
      • Dec 11 (Wed) 7:00 ~ 8:30 pm
      • Important Topics (final) [link]


Schedule

    • Week 1: Introduction to AI [note]
    • Week 2~4: Basic methods in ML (Logistic regression, Neural Networks, SVM) [note1, note2]
    • Week 5~7 (PBL Case Study #1) (How to read MNIST files)
      • Teams (class1 , class2)
      • Notes
        • Preprocessing [note]
        • Model selection [note]
        • Cross validation & Grid Search summary [note]
      • Main goal: model selection
        • F1-score
        • Hyperparameters
          • Logistic regression: lambda, ||w||_2^2, ||w||_1
          • SVM: C
        • To save time, you can use the test (10k) set for training, and the training set (60k) for testing.
      • Team presentations (send presentation files to nomar0107@gmail.com)
      • Evaluations: (class1, class2) (You must login with Google account)
    • Week 8: Midterm
    • Week 9: Gradient-Based Learning [pdf]
    • Week 10~12: (PBL Case Study #2)
      • Leaderboard (class1, class2)
      • Team composition (class1, class2)
      • Team & Member evaluation (class1, class2)
      • Notes
        • SGD Algorithm for solving SVM [note]
        • Unsupervised Learning (PCA)
        • Unsupervised Learning: LDA, Kernel PCA [note]
      • Code submission (class 1: by Nov 17, class 2: by Nov 26 midnight)
        • TA : nomar0107@gmail.com
        • Python code: team#.py <training_data> <test data>
        • 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
        • Fix hyperparameters in your code.
        • Output labels per line, as explained in PBL 2 problem description.
    • Week 13~16: PBL #3
    • Week 15 (Dec 11, Wed): Final Exam
      • Dec 11 (Wed) 7:00 ~ 8:30 pm