This repository is to backup my learning notes.
- Point Estimators and Confidence Intervals pdf
- Maximal margin classifier
- Lagrangian Duality
- See more details in Andrew Ng's notes
- Review on Logistic Regression (Maximum log conditional likelihood)
- A summary of popular Activation functions
- Training neural networks
- Classification performance measures: Mis-classification error, Confusion Matrix, Precision, Recall, F1 score
- Common Nueral Network structures: CNN, RNN, GAN, Auto-encoder
- Entropy: quantify the uncertainty
- An feature selection algorithm using Mutual information
- Ridge and Lasso regularization, Elastic Net
- Forward, Backward and Mixed selection
- AdaBoosting classifier: algorithms, exponetial loss, parameter updates
- Decision Tree growing and pruning, Error Measures (misclassification errer, Gini index, Cross-entropy)
- Random Forest, Bagging, Bootstrap, OOB (out-of-bag) error
- Boosting trees
- Possible case of a linear system pdf
Python, Git and Latex pdf
- Include common commands that I often use.