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ML book
Introduction to ML
Overview
Definitions of ML
Applications of ML
Data
Types of ML
k-Nearest Neighbors
Model selection
Linear regression
Simple linear regression
Polynomial regression
Multiple linear regression
Regularization
Numeric optimization
General linear model
Linear classification
Logistic regression
Multinomial logistic regression
Numeric optimization
Support Vector Machines (SVM)
Evaluation Metrics
Classification Metrics
ROC-AUC
Regression metrics
Cross-validation
Hyperparameters tuning
Decision Trees
Classification tree
Regression tree
Impurity and information criterions
Tree pruning
Gradient boosting
Boosting and additive modeling
Generic gradient boosting
AdaBoost
XGBoost
CatBoost
Unsupervised Learning
Principal components analysis (PCA)
Clustering metrics
K-means
Hierarchical Clustering
Deep Learning
Multilayer perceptron (MLP)
Layers of MLP
Forward and backward pass
Activation functions
Back propagation
Weights initialization
Regularization in MLP
Optimization in DL
CNN
Convolution of matrices
Convolutions of tensors
Pooling
Back propagation in CNN
Data augmentation
Architectures of CNN
Sequential NNs
Vanilla RNN
LSTM
GRU
Attention
Transformers
Generative models
Autoencoders
Variational autoencoders (VAE)
Ganerative adversarial networks (GAN)
Reinforcement Learning
RL basics
Multiarmed bandits
Markov decision process (MDP)
Q-learning
Python for ML
Python Basics
Basic Types
Variables
Control flow
Functions
Classes
ISLP Lab
NumPy
Pandas
Math for ML
Linear Algebra
Vectors
Matrices
Linear systems
Inverse matrix
Rank of a matrix
Orthogonality
Orthogonal projections
Determinants
Eigenvalues and eigenvectors
Diagonalization
Matrix norms
Singular Value Decomposition (SVD)
Calculus
1-d calculus
Multivariate calculus
Matrix calculus
Probability
Statistics
Maximum likelihood estimation (MLE)
Optimization
Gradient descent
Stochastic gradient descent
Newton’s method
References
Notation
Bibliography
ML resources
.ipynb
.pdf
Principal components analysis (PCA)
Principal components analysis (PCA)
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