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ML Book
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ML 2023
Introduction to ML
HW1
Deep Learning 2024
ML 2024 (MS)
ML 2024 (MBA)
Classical ML
Introduction
Definitions of ML
Applications of ML
Types of ML
Python libraries
Data
Tabular data
Image data
Text data
Datasets
Supervised learning
Types of supervised learning
Model selection
k-Nearest Neighbors
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
Probabilistic models
Bayesian inference
Probabilistic models for linear regression
Probabilistic models for logistic regression
Linear Discriminant Analysis (QDA)
Quadratic Discriminant Analysis (QDA)
Naive Bayes classifier
Decision Trees
Classification tree
Regression tree
Impurity and information criterions
Tree pruning
Ensembling
Bias-variance decomposition
Voting, stacking and blending
Bagging
Random forests
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
Training of MLP
Back propagation
Weights initialization
Regularization in MLP
Optimization in DL
Multilayer Perceptron (MLP)
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
Multi-armed bandits
Markov Decision Process (MDP)
Q-learning
Python for ML
Python Basics
Basic Types
Variables
Control flow
Functions
Classes
ISLP Lab
NumPy
Pandas
Data visualization
Math for ML
Linear Algebra
Vectors
Matrices
Inverse matrix
Rank of a matrix
Orthogonal projections
Determinants
Eigenvalues and eigenvectors
Diagonalization
Matrix norms
Singular Value Decomposition (SVD)
Calculus
Derivative
Multivariate calculus
Matrix calculus
Optimization
Probability
Random variables
Entropy
Independence and random vectors
Statistics
Limit theorems
Estimations
References
Notation
Bibliography
ML resources
.ipynb
.pdf
Bagging
Bagging
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ML Handbook section
