Class Type
- Flipped Learning
Language
- Lecture: Korean
- Material: English & Korean
Textbook
- C. Bishop, Pattern Recognition and Machine Learning
- Aurélien Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Schedule
Lecture videos: pre-class
Lecture slide: pre-class
- Preclass 01 | Introduction to Machine Learning I (slide, note)
- Preclass 02 | Introduction to Machine Learning II (slide, note)
- Preclass 03 | Linear Algebra (slide, note)
- Preclass 04 | Probability (slide, note)
- Preclass 05 | K-Means Clustering (slide, note)
- Preclass 06 | Mixture of Gaussian Clustering (slide, note)
- Preclass 07 | Convex Optimization (slide, note)
- Preclass 08 | Dual Problem and KKT Conditions (slide, note)
- Preclass 09 | Singular Value Decomposition (slide, note)
- Preclass 10 | Decision Tree (slide, note)
- Preclass 11 | Random Forest (slide, note)
- Preclass 12 | Information Theory (slide, note)
- Preclass 13 | Chain Rule (slide, note)
- Preclass 14 | Image Convolution (slide, note)
Lecture videos: in-class
Lecture slide: in-class
- Inclass 01 | Course Introduction
- Inclass 02 | Python Basics
- Inclass 03 | Python Advances
- Inclass 04 | K-Means Clustering (note)
- Inclass 05 | Mixture of Gaussian Clustering (note)
- Inclass 06 | Linear Regression (slide, note)
- Inclass 07 | Normal Equation (slide, note)
- Inclass 08 | Gradient Descent (slide, note)
- Inclass 09 | Polynomial Regression and Regularization (slide, note)
- Inclass 10 | Linear Classification and Perceptron Method (slide, note)
- Inclass 11 | Logistic Regression (slide, note)
- Inclass 12 | Support Vector Machine I (slide, note)
- Inclass 13 | Support Vector Machine II (slide, note)
- Inclass 14 | Nonlinear SVM and Kernel Method (slide, note)
- Inclass 15 | Mid-Term Cover
- Inclass 16 | Model Evaluation and Selection (slide, note)
- Inclass 17 | Maximum Likelihood Estimate (slide, note)
- Inclass 18 | Maximum A Posteriori Estimate (slide, note)
- Inclass 19 | Posterior Predictive Distribution (slide, note)
- Inclass 20 | Principal Component Analysis (이론) (slide, note)
- Inclass 21 | Principal Component Analysis (실습) (slide, note)
- Inclass 22 | Decision Tree and Random Forest (실습) (slide, note)
- Inclass 23 | Neural Network (이론) (slide, note)
- Inclass 24 | Forward Propagation and Backpropagation (이론) (slide, note)
- Inclass 25 | Neural Network and Backpropagation (실습) (slide, note)
- Inclass 26 | Convolutional Neural Network (이론, 실습) (slide, note)
- Inclass 27 | Final Exam Cover
Last Updated on January 7, 2022