Teaching Docs

These documentation pages are organized into three self-contained modules:

Machine Learning - fundamentals of supervised / unsupervised learning with hands-on model building.
Deep Learning - neural-network foundations, optimization, and convolutional architectures for vision.
Explainability in AI - methods for interpreting models and strengthening trust, fairness, and accountability.

Each module can be followed independently, but new learners are encouraged to begin with Machine Learning, then continue to Deep Learning and Explainability.

Machine Learning Deep Learning Explainability in AI

Introduction & Background

Simple Linear Regression

Inference & Diagnostic

Multiple Regression and Feature Engineering

Model Selection and Regularization

Generalized Linear Models (GLM) and Logistic Regression

Mathematical Annexes

Introduction and Partitioning

Splitting Criteria and Best Split

Growth Control and Pruning

Foundations of Model Evaluation

Metrics for Regression and Classification

Cross-Validation Strategies

Feature Selection and Preprocessing

Hyperparameter Tuning and Early Stopping

Imbalanced Data and Threshold Selection

Introduction and Hierarchical Clustering

K-means and K-medoids

Gaussian Mixtures and the EM Algorithm

Density-Based Clustering and Practical Guidance

Teaching Docs