1) Train test split
2) Precision / Recall / Accuracy metrics
3) K-d Tree
4) KNN using K-d Tree

1) KMeans
2) DBScan
3) AgglomerativeClustering

1) MSE / R2 metrics
2) Simple Linear Regression
3) Linear Regression with Lasso regularization
4) Interpretation

1) Simpler perceptron
2) Pocket Learning Algorithm perceptron
3) Transform images to vector
4) Testing on digits dataset from sklearn

1) Linear, ReLU, Softmax Layers
2) MLPClassifier
3) Testing on synthetic data
4) Pytorch CNN for CIFAR10

1) Encoder
2) Decoder
3) VAE

1) Linear SVM
2) Kernel functions
3) Kernel SVM

1) Criterions, gain
2) Decision Tree 
3) Decision Tree Classifier

1) Decision Tree 
2) Random Forest
3) Feature importance (out of bag error)
4) Catboost

1) HillClimb
2) Genetic Alghorithm
3) Bag-of-words
4) Naive Bayes