The Central Limit Theorem
The Bootstrap
Midterm Review
Assessing Models
Chance & Models
Table Methods, Conditionals & Iteration
More Visualizations, Intro to Functions
Visualizations, Data Types, Extending Tables
Intro to Tables & Causation
By the Numbers
Wrapping Up
PCA & Clustering
Regression & Residuals
ROC Curves and Performance Metrics
Correlation and Regression
Logistic Regression
Central Limit Theorem
SQL
Interpreting Confidence, Center and Spread
Random Variables, Bias, Variance
Confidence Intervals
Cross-Validation & Regularization
Gradient Descent
Sampling, Hypothesis Testing, Decisions
Modeling and OLS
Conditions, Iterations
Probability, Sampling, & Visualization
Functions, Pivots, and Joins
RegEx & Visualizations
Data Visualizations, Histograms
Pandas II & EDA
Data Types, Table, Census
Pandas
Cause and Effect, Python
Prerequisites
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
VPIs and HMMs
Final Review of RL
Final Practice
Final Review of MDPs
Vector Calculus, Backpropagation
Neural Networks, Loss Functions
RMSE, Prediction Intervals, and k-Nearest Neighbors
MLE, Naive Bayes, Regressions
Correlation & Regression
MDPs, VPI
Sample Means, CLT
Particle Filtering, Decision Networks
D-Separation, HMMs
Midterm Review of CSPs
Probability, Bayes Nets, Variable Elimination
Functions, Table Methods
Functions, Visualizations
Data Types and Table Manipulation
Informed Search
Table Operations
Uninformed Search
