Class Type

  • Flipped Learning

Language

  • Lecture: Korean
  • Material: English / Korean

Textbook

  • Aurélien Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
  • C. Bishop, Pattern Recognition and Machine Learning

Schedule

Exam

Lecture videos: pre-class

Lecture slides: pre-class

  1. Preclass 01 | Mathematical Background I – Linear Algebra (slide, note)
  2. Preclass 02 | Mathematical Background II – Derivative (slide, note)
  3. Preclass 03 | Mathematical Background III – Probability (slide, note)
  4. Preclass 04 | Introduction to Machine Learning I
  5. Preclass 05 | Introduction to Machine Learning II (slide, note)
  6. Preclass 06 | K-means Clustering (slide, note)
  7. Preclass 07 | Mixture of Gaussian Clustering (slide, note)
  8. Preclass 08 | Convex Optimization and Duality I (slide, note)
  9. Preclass 09 | Convex Optimization and Duality II (note)
  10. Preclass 10 | Maximum Likelihood Estimate (slide, note)
  11. Preclass 11 | Singular Value Decomposition I (slide, note)
  12. Preclass 12 | Singular Value Decomposition II (note)
  13. Preclass 13 | Chain Rule (slide, note)
  14. Preclass 14 | Image Convolution (slide, note)

Lecture videos: in-class

Lecture slides: in-class

  1. Inclass 01 | Course Introduction (slide, note)
  2. Inclass 02 | Python Introduction
  3. Inclass 03 | Cover: Mathematics and Machine Learning (note)
  4. Inclass 04 | Cover & Python: K-Means (note)
  5. Inclass 05 | Cover & Python: Mixture of Gaussian (note)
  6. Inclass 06 | Regression I (slide, note)
  7. Inclass 07 | Regression II (slide, note)
  8. Inclass 08 | Classification I (slide, note)
  9. Inclass 09 | Classification II (slide, note)
  10. Inclass 10 | Support Vector Machine (slide, note)
  11. Inclass 11 | Python: Support Vector Machine and K-Fold Cross Validation
  12. Inclass 12 | Significance Test (slide, note)
  13. Supplement 01 | Pre Mid-Term
  14. Supplement 02 | Post Mid-Term
  15. Inclass 13 | Model Evaluation and Selection (slide, note)
  16. Inclass 14 | Bayesian Inference I (slide, note)
  17. Inclass 15 | Bayesian Inference II (slide, note)
  18. Inclass 16 | Dimensional Reduction (slide, note)
  19. Inclass 17 | Neural Network (slide, note)
  20. Inclass 18 | Forward Propagation and Entropy (slide, note)
  21. Inclass 19 | Backpropagation (slide, note)
  22. Inclass 20 | Convolutional Neural Network (slide, note)
  23. Inclass 21 | Recurrent Neural Network (slide, note)
  24. Supplement 03 | Pre Final Exam