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)