Machine Learning

Lecture Notes

A compact index for the static lecture pages, ready to serve directly from Dokploy.

1

Machine Learning & Supervised Learning

Foundations, generalization, supervised learning, and model assessment.

 2

Bayesian Decision Theory

Risk, loss functions, discriminants, and probabilistic classification.

 3

Parametric Methods

Maximum likelihood, regression, classification, and model fitting.

 4

Graphical Models

Conditional independence, Bayesian networks, and inference patterns.

 5

Hidden Markov Models

Sequential observations, latent states, and dynamic probabilistic models.

 6

Rule-Based Learners & Decision Trees

Rules, tree induction, entropy, pruning, and interpretable classifiers.

 7

Lazy Learners & Density Estimation

Nearest neighbors, local models, nonparametric density, and kernels.

 8

Support Vector Machines & Kernel Methods

Margins, support vectors, dual form, and kernelized learning.

 9

Combining Methods

Ensembles, voting, bagging, boosting, random forests, and AdaBoost.