## 주메뉴

### 데이터마이닝(2)

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자연과학 >수학ㆍ물리ㆍ천문ㆍ지리 >통계학
• 강의학기
2021년 2학기
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강의계획서
1. 파이썬의 scikit-learn, tensorflow를 활용하여 머신러닝을 적용하는 방법들에 대해 학습합니다.

2. 데이터마이닝1 수업에서 이론적인 부분들을 공부하였다면, 본 수업에서는 python을 이용하여 실제 데이터에 머신러닝 기법들을 적용하여 분석하는 방법들에 대해 학습합니다.
수업 설명, Colab 설명

#### 차시별 강의

 1. 수업 설명, Colab 설명 수업 설명, Colab 설명 Chapter 1 Machine Learning Landscape 1. Types of Machine Learning system 2. Main challenges of Machine Learning, 3. Testing and validation 2. Chapter 2 End-to-End Machine Learning Project 1. Look at the big picture 2. Get the data 3. Discover and visualize the data to gain insights 4. Prepare the data for Machine Learning algorithms 3. Chapter 2 End-to-End Machine Learning Project 5. Select a model and train it 6. Fine-tune your model Chapter 3 Classification 1. MNIST data 2. Training a Binary Classifier 3. Performance Measures 4. Multiclass Classification 5. Multilable Classification 4. Chapter 4 Training Models 1. Linear Regression 2. Gradient Descent 3. Polynomial Regression 4. Learning Curves 5. Regularized Linear Models 5. Chapter 5 Support Vector Machines 1. Linear SVM Classification 2. Nonlinear SVM Classification Chapter 5 Support Vector Machines 3. SVM Regression Chapter 6 Decision Tree 1. Training and Visualizing a Decision Tree 2. Making Predictions 3. Estimating Class Probabilities 4. The CART Training Algorithm 5. Regularization Hyperparameters 6. Chapter 7 Ensemble Learning and Random Forests 1. Voting Classifiers 2. Bagging and Pasting 3. Random Forests 4. Boosting 5. Stacking Chapter 8 Dimensionality Reduction 1. The Curse of Dimensionality 2. Main Approaches for Dimensionality Reduction 3. PCA 7. Chapter 8 Dimensionality Reduction 4. Kernel PCA 5. Locally Linear Embedding (LLE) 6. Other Dimensionality Reduction Techniques Chapter 9 Unsupervised Learning Techniques 1. Clustering 2. Gaussian Mixtures 8. Chapter 10 Introduction to Artificial Neural Networks with Keras 1. From Biological to Artificial Neurons 2. Implementing MLPs with Keras 9. Chapter 10 Introduction to Artificial Neural Networks with Keras 3. Fine-Tuning Neural Network Hyperparameters Chapter 11 Training Deep Neural Nets 1. The Vanishing/Exploding Gradients Problems 2. Reusing Pretrained Layers 10. Chapter 11 Training Deep Neural Nets 3. Faster Optimizers 4. Avoiding Overfitting through Regularization Chapter 12 Custom Models and Trainingwith TensorFlow 1. A Quick Tour of Tensorflow 2. Using Tensorflow like Numpy 11. Chapter 12 Custom Models and Trainingwith TensorFlow 3. Customizing Models and training algorithms 4. Tensorflow functions and graphs 12. Chapter 13 Loading and Preprocessing Data with TensorFlow 1. Data API 2. TFRecord Format 3. Preprocessing the input features 4. TF transform, TF Datasets 13. Chapter 14 Deep Computer Vision Using Convolutional Neural Networks 1. Convolutional Layers 2. Pooling Layers 3. CNN Architectures 4. Implementing a ResNet-34 CNN using Keras 14. Final Presentation 1 Final Team presentation Final Presentation 2 Final Team presentation 2

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