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%)

• • Grading Rublic

Links

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

  • Q/As:

    • Online: trello

    • Office Hour of TAs: Wed 2~3pm. Cluster Bd. Room 620 (AI LAB)

    • TA : nomar0107@gmail.com, RA : max2747@naver.com

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)

      • TA : nomar0107@gmail.com

      • Python code: team#.py <training_data> <test data>

    • 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 12: Unsupervised Learning: LDA, Kernel PCA [note]

    • Weak 13: Clustering, Evalution Metric, Variable selection

    • Weak 14: Decison tree, Ensemble Methods

  • Week 15: Activity report, Final Exam (Report)