Basic Course

Introduction: 

• Lectures:

  1.  big data,  

  2. statistics for ML and exploratory data analysis (EDA),

  3. resampling and cross-validation

• Examples: 

  1. learn ggplot, 

  2. notebooks and EDA, 

  3. data structures in R, 

  4. crabs EDA , 

  5. diabetes EDA (python)

  6. Numerous Cross-Validation examples in R and Python--please consult the README for details.

Regression:

• Lectures:

  1. linear regression, 

  2. other regression methods, 

• Examples:

  1. Photosynthesis data - simple linear regression

  2. Concrete data - comparison of simple and multiple regression.

  3. LIDAR data - nonlinear SVM regression

Classification:

• Lectures: 

  1. k-NN, 

  2. linear methods

  3. SVM

• Examples: 

  1. k-nn classifier for iris data

  2. SVM classifier for iris data

CART:

• Lectures:

  1. Decision trees, bagging, boosting and random forests.

• Examples:

  1. coming soon...

Neural networks

• Lectures: 

  1. feed-forward, 

  2. CNNs (coming soon...)

• Examples:

  1. Simple NN for rule of squares.

  2. NN classifier for wine fraud data.

Lectures: 

1. PCA, 

2. k-means, hierarchical clustering

Examples: 

1. k-means on Gaussian blobs.

Introduction to DA

Adjoint method for DA

Variational DA

• 3DVar

• 4DVar

Basic filters (KF)

Nonlinear filters (EKF)--see Advanced Course

Ensemble filters (EnKF)--see Advanced Course