[SCS4049] Machine Learning and Data Science (2021-spring)
Class Type
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
Lecture videos: pre-class
Lecture slides: pre-class
- Preclass 01 | Mathematical Background I – Linear Algebra (slide, note)
- Preclass 02 | Mathematical Background II – Derivative (slide, note)
- Preclass 03 | Mathematical Background III – Probability (slide, note)
- Preclass 04 | Introduction to Machine Learning I
- Preclass 05 | Introduction to Machine Learning II (slide, note)
- Preclass 06 | K-means Clustering (slide, note)
- Preclass 07 | Mixture of Gaussian Clustering (slide, note)
- Preclass 08 | Convex Optimization and Duality I (slide, note)
- Preclass 09 | Convex Optimization and Duality II (note)
- Preclass 10 | Maximum Likelihood Estimate (slide, note)
- Preclass 11 | Singular Value Decomposition I (slide, note)
- Preclass 12 | Singular Value Decomposition II (note)
Lecture videos: in-class
Lecture slides: in-class
- Inclass 01 | Course Introduction (slide, note)
- Inclass 02 | Python Introduction
- Inclass 03 | Cover: Mathematics and Machine Learning (note)
- Inclass 04 | Cover & Python: K-Means (note)
- Inclass 05 | Cover & Python: Mixture of Gaussian (note)
- Inclass 06 | Regression I (slide, note)
- Inclass 07 | Regression II (slide, note)
- Inclass 08 | Classification I (slide, note)
- Inclass 09 | Classification II (slide, note)
- Inclass 10 | Support Vector Machine (slide, note)
- Inclass 11 | Python: Support Vector Machine and K-Fold Cross Validation