人工智能4周理论+实践应用详解

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【课程内容】

Week1

1 - 1 - Prediction motivation
1 - 2 - What is prediction
1 - 3 - Relative importance of steps
1 - 4 - In and out of sample errors
1 - 5 - Prediction study design
1 - 6 - Getting Data Overview
1 - 6 - Types of errors
1 - 7 - Receiver Operating Characteristic
1 - 8 - Cross validation
1 - 9 - What data should you use

Week2

2 - 1 - Caret package
2 - 2 - Data slicing
2 - 3 - Training options
2 - 4 - Plotting predictors
2 - 5 - Basic preprocessing
2 - 6 - Covariate creation
2 - 7 - Preprocessing with principal components analysis
2 - 8 - Predicting with Regression
2 - 9 - Predicting with Regression Multiple Covariates

Week3

3 - 1 - Predicting with trees
3 - 2 - Bagging
3 - 3 - Random Forests
3 - 4 - Boosting
3 - 5 - Model Based Prediction

Week4

4 - 1 - Regularized regression
4 - 2 - Combining predictors
4 - 3 - Forecasting
4 - 4 - Unsupervised Prediction









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