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1주차
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1. Introduction
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1.0 Prerequisites |
| 1.1 About This Lecture - 1.2 History on AI |
| 1.3 Dataset |
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2주차
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2. Preliminaries
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2.1 Matrix Alebra |
| 2.2 Model Assessment and Selection I |
| 2.2 Model Assessment and Selection II |
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3주차
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2. Preliminaries
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2.3 Taylor Expansion |
| 2.4 Maximum Likelihood Methods |
| 2.5 The EM Algorithm |
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4주차
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2. Preliminaries |
2.6 Bayesian Methods |
| 3. Regression |
3.1 Parametric Regression / Linear Regression |
| 3.1 Parametric Regression / Subset Selection |
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5주차
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3. Regression
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3.1 Parametric Regression / Ridge Regression |
| 3.1 Parametric Regression / Lasso |
| 3.1 Parametric Regression / Other Shrinkage Methods |
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6주차
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3. Regression
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3.2 Nonparametric Regression / Smoothing Splines I |
| 3.2 Nonparametric Regression / Smoothing Splines II |
| 4.1 Introduction - 4.2 Parametric Classifiers |
|
7주차 |
4. Classification
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4.3 Nonparametric Classifiers / Kernel Density |
| 4.3 Nonparametric Classifiers / CART |
| 4.4 Support Vector Machine / Linearly Separable Case |
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중간시험
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| 8주차 |
4. Classification
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4.4 Support Vector Machines / Linearly Nonseparable Case |
| 4.4 Support Vector Machines / Nonlinear Case |
| 4.4 Support Vector Machine / Regression |
| 9주차 |
4. Classification
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4.5 Ensemble Learning / Bagging |
| 4.5 Ensemble Learning / Boosting |
| 4.5 Ensemble Learning / Boosting and Additive Trees |
|
10주차
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4. Classification
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4.5 Ensemble Learning / Boosting Algorithms and Random Forest |
| 5.1 Projection Pursuit Regression |
| 5. Neural Networks |
5.2 Neural Networks - 5.3 Fitting Neural Networks |
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11주차
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5. Neural Networks
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5.4 Some Issues in Training Neural Networks |
| 5.4 Some Issues in Training Neural Networks / Examples |
| 6.1 Introduction - 6.2 Combinatorial Algorithm - 6.3 k-means |
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12주차
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6. Cluster Analysis
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6.4 Vector Quantization - 6.5 Hierarchical Clustering |
| 6.6 Self-Organizing Maps - 6.7 Spectral Clustring I |
| 6.7 Spectral Clustring II |
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13주차
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6. Cluster Analysis
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6.8 Principal Components, Curves, and Surfaces I |
| 6.8 Principal Components, Curves, and Surfaces II |
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기말시험
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