Introduction to neural networks covering linear regression, softmax classification, activation functions, and why deep layers are preferred.

Neural networks as function approximators: linear models, activation functions, multi-layer perceptrons, universal approximation theorem, and loss functions.

Difference between numpy.matmul and numpy.dot for high-dimensional matrix multiplication.

Probability theory foundations for machine learning: probability distributions, joint and conditional distributions, Bayes' rule, and expectation.

Practical PyTorch tips covering parameter initialization, model.eval(), tensor views, and the training loop.

Foundations of statistical modeling: parametric vs. nonparametric methods, probability distributions, MLE, and log-likelihood.