CS 464 • Midterm • Introduction to Machine Learning

Introduction to Machine Learning

Machine Learning Notation Explained

Machine Learning Preliminaries

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

Maximum Likelihood Estimation(MLE)

Bernoulli Likelihood

Multinomial Likelihood and Smoothing

Bayes' Theorem

Parametric Classification

Unequal Variances → Quadratic Boundary

Gaussian Classification Boundary

Modeling Multivariate Data: Estimation, Normal Distributions, and Naive Bayes

Multivariate Classification: Linear, Quadratic, and Model Selection

Discrete Features & Multivariate Regression

Sample Mean and Covariance Matrix

Mahalanobis Distance

LDA vs QDA Classification

Naive Bayes Classification (Discrete Features)

Feature Selection vs Feature Extraction

Principal Component Analysis (PCA)

Feature Embedding & Factor Analysis (FA)

Evaluation/Performance Metrics

Why Supervised?

Hypothesis Space & Occam's Razor

Loss Functions: Measuring Mistakes

Linear & Polynomial Regression

Example: Least-Squares Linear Regression

The Problem in Linear Regression

Linear Discriminant

Two Classes/Multiple Classes/Pairwise Seperation

From Discriminants to Posteriors

Gradient Descent

Pass Rates & Majors (Bayes; Law of Total Probability)

Weighted Least Squares (Closed-Form Solution, Matrix View & Interpretation)

MLE for α (positive support, exponential tail)

Naive Histogram Estimator vs. Parzen Windows (Kernel)

Naive Density Estimator (Bandwidth effect & validity)

Why Not Regression for Classification?

From Binary to Multiclass: One-vs-All / One-vs-One with a Binary Classifier

Max-shift for SoftMax

Mahalanobis vs. Euclidean: Why and When?

Regularized Least Squares

Gaussian Generative Model → Logistic Posterior

Naive Bayes Text Classification with Binary Features

Derivative of Softmax

Kernel Density Estimation

MLE for a Discrete PMF

1-NN LOOCV on Patient Dataset

Linear Regression + MSE: Gradient Descent Step Size Effects