ENGR 421 • Midterm II • Introduction to Machine Learning

Counting and Probability

Conditional Probability and Independence

Bayes' Rule

Discrete Random Variables

Continuous Random Variables

Expected Value and Variance

Bernoulli and Binomial Distributions

Continuous Uniform Distribution

Exponential Distribution

Normal Distribution

Laplace and Logistic Distributions

Introduction to Deep Learning & Activation Functions

Training Deep Networks

Regularization Techniques

Tuning Network Structure

Learning Time

Time-Delay Neural Networks (TDNN)

RNN / LSTM / GRU

Generative Adversarial Networks (GANs)

What Nonparametric Means

Density Estimation

Nonparametric Classification

Condensed Nearest Neighbor

Outlier Detection

Nonparametric Regression

Additive Models & How to chose h/k

What is a Decision Tree?

Splitting in Classification Trees

Pruning Trees

From Trees to Rules

Multivariate/Oblique Trees

Choosing Between Two Splits: Gini vs. Misclassification

What & Why

Maximum Margin Classification

Maximizing the Margin

Lagrangian Formulation of the Hard-Margin SVM

From Primal to Dual: Solving the SVM Optimization

Why only a few points matter (KKT & sparsity)

From 𝛼 to parameters

Prediction uses only support vectors

Soft Margin SVM

Soft Margin Dual

Linear Discriminant with Equal Variance

Naive Histogram Estimator vs. Parzen Windows (Kernel)

Kernel Smoother

Naive Density Estimator (Bandwidth effect & validity)

Comparing Two Splits (Gini vs. Misclassification)

Prepruning vs. Postpruning (Which and Why?)

Discrete Attribute in Decision Trees

Kernel Density Estimation

Naive Bayes Text Classification with Binary Features