By the Numbers

Wrapping Up

PCA & Clustering

ROC Curves and Performance Metrics

Regression & Residuals

Logistic Regression

Correlation and Regression

SQL

Central Limit Theorem

Random Variables, Bias, Variance

Interpreting Confidence, Center and Spread

Cross-Validation & Regularization

Confidence Intervals

Gradient Descent

Midterm Review

Modeling and OLS

Sampling, Hypothesis Testing, Decisions

Probability, Sampling, & Visualization

Conditions, Iterations

RegEx & Visualizations

Functions, Pivots, and Joins

Pandas II & EDA

Data Visualizations, Histograms

Pandas

Data Types, Table, Census

Prerequisites

Cause and Effect, Python

Introduction

ML II

ML

Particle Filtering and Naive Bayes

HMMs, VPI

Midterm Review of Search, Games, and CSPs

BN Inference and Sampling

Probability and Bayes Nets

RL

MDPs

Games

CSPs

Search

ML II

ML

Particle Filtering and Naive Bayes

VPIs and HMMs

BN Inference and Sampling

Probability and Bayes Nets

RL

MDPs

Games

CSPs

Search

Final Review of RL

Final Review of MDPs

Final Practice

Vector Calculus, Backpropagation

Wrapping Up

Neural Networks, Loss Functions

RMSE, Prediction Intervals, and k-Nearest Neighbors

MLE, Naive Bayes, Regressions

Regression & Residuals

RL

Correlation & Regression

MDPs, VPI

Sample Means, CLT

Particle Filtering, Decision Networks

The Bootstrap

D-Separation, HMMs

Midterm Review

Midterm Review of CSPs

Assessing Models

Probability, Bayes Nets, Variable Elimination

Functions, Table Methods

Games

Functions, Visualizations

CSPs

Data Types and Table Manipulation

Informed Search

Table Operations

Uninformed Search

By the Numbers