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%)

• • Grading Rublic

Links

• • PBL Center: http://pbl.hanyang.ac.kr

  • TA & Q/A:

    • Cluster Bd. Room 620 (AI LAB) / nomar0107@gmail.com,

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

    • Leaderboard (class1, class2)

    • Team & Member evaluation (class1, class2)

    • Guideline(PPT)

    • Code submission (class 1: by Dec 15, class 2: by Dec 17 midnight)

    • Clustering, Decison tree, Ensemble Methods

  • Week 15 (Dec 11, Wed): Final Exam

    • Dec 11 (Wed) 7:00 ~ 8:30 pm