17 docs tagged with "machine-learning"

Clustering and Dimensionality Reduction

Use worked k-means and PCA examples to distinguish discovering groups from compressing representations, and explain the limits of distance, scaling, and visualization.

Convex Optimization

A foundation map for convex sets, convex functions, duality, and optimization problems with global guarantees.

Decision Trees, Random Forests, and Gradient Boosting

Derive a tree split and a boosting update, then compare their inductive biases, validation needs, and limits.

Information Theory and Entropy

A foundation map for measuring uncertainty, information, compression limits, and distributional difference.

Linear Algebra

A foundation map for vectors, linear transformations, matrix factorization, and data-oriented applications.

Linear and Quadratic Discriminant Analysis

Gaussian generative classifiers whose shared or class-specific covariance assumptions produce linear or quadratic decision boundaries.

Linear Regression: Modeling and Gradient Descent

Build a linear predictor, calculate residuals, and follow gradient updates; closed-form estimation and statistical inference are covered separately.

Log Loss in Machine Learning

Why logarithmic loss measures probabilistic classification error and how calculus connects it to likelihood optimization.

Machine learning and training

What changes during training, and why does it generalize?

Machine Learning General Concepts

A compact vocabulary for models, losses, empirical risk, likelihood, regularization, optimization, and generalization.

Neural and generative models

How do architectures represent inputs and produce outputs?

Pretraining, Supervised Fine-Tuning, and Preference Optimization

Explain how training targets shape representations, instruction following, and preferred behavior without equating preference with truth.

Probability Calibration, Decision Thresholds, and Abstention

Distinguish prediction accuracy, reliable probabilities, and worthwhile actions, and use independent data to set calibration and abstention rules.

Reinforcement Learning: Rewards, Value, and Sequential Decisions

Use a two-state example to distinguish immediate rewards, long-term value, exploration, and learned agent policies.

Softmax Regression

Multiclass linear classification with logits, softmax probabilities, cross-entropy, and clear boundaries around multilabel tasks.

Training a Model: Gradients, Optimizers, and Learning Rates

Follow one parameter update, then connect minibatches, optimizer state, validation, and resumable checkpoints.

Transfer Learning, LoRA, and Distillation

Choose between a frozen encoder, full fine-tuning, low-rank adaptation, and a smaller student for a defined task.