머신 러닝 I 강의 계획서
| 주차 | Topic | 내용 |
|---|---|---|
| 01주차 | Understanding AI and ML (pdf) | Course Orientation, ML Overview |
| 02주차 | Basic Math in ML (pdf) | Probability, Linear Algebra, Derivative |
| 03주차 | Numpy Basics (ipynb) | Numeric Operation in ML |
| 04주차 | Data Visualization | Matplotlib Basic Visualiaztion for ML using Matplotlib |
| 05주차 | Pandas Basics for ML | Series, DataFrame, Indexing, Selection, Missing Data, Basic Operations |
| 06주차 | Pandas Applications and Regression Preview | By exploiting Pandas, ◦ Data Handling ◦ Working with DB ◦ Regression Preview |
| 07주차 | Model Performance Evaluation | Classificantion Metrics, Regression Metrics, Model Performance Evaluation Practice |
| 08주차 | Mid-term Exam |
(중간고사: 자료 없음) |
| 09주차 | Linear Regression Theory | Concept of Dataset, Regression Purpose, Training, Prediction, Evaluation, Gradient Descent |
| 10주차 | Linear Regression Practice | Practice 1 ◦ Codes: notebook 1 ◦ Dataset: salary.csv Practice 2 ◦ Codes: notebook 2 ◦ Dataset: kc_house.csv |
| 11주차 | Logistic Regression Theory | Feature Space, Decision Boundaries, Classification Methods, Loss Function, Graident Descent |
| 12주차 | Logistic Regression Practice | Practice 1 ◦ Codes: notebook 1 ◦ Dataset: social_network_ads.csv Practice 2 ◦ Codes: notebook 2 ◦ Dataset: diabetes.csv 📘 Professor's Recommendation |
| 13주차 | Decision Trees | Practice ◦ Codes: zip file |
| 14주차 | K-Nearest Neighbors (KNN) | Practice ◦ Codes: zip file |
| 15주차 | Final Exam |
(기말고사: 자료 없음) |