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Workbook 36
Chapter 1: The Multivariate Normal Distribution
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Objectives
Understand how to process different multi-variable normal distribution.
Prerequisites
Chapter 2: Numerical Double Integration
Objectives
Be able to use integration (and double integration) to approximate probability density functions.
Prerequisites
Chapter 3: Sets and Logic
Objectives
Understand the chapter on logic. This includes logical symbols, booleans, intersection, truth tables, venn diagrams, complements, subsets, cardinality, and contrapositives.
Chapter 4: Introduction to Graphs
Objectives
Understand how graphs, nodes, and edges work together to form connections between data. Understand how weighted edges effect graphs.
Prerequisites
Chapter 5: Dijkstra's Algorithm
Objectives
Understand how to use dikstra's algorithm to find the shortest path.
Prerequisites
Chapter 6: Binary Search
Objectives
Implement binary search to search though lists, graphs, trees, and queues. Understand the requirements for a list to get binary searched.
Prerequisites
Chapter 7: Other Graph Algorithms
Objectives
Understand how the processes of DFS and Bellman-Ford algorithms work.
Prerequisites
Chapter 8: Bayesian Classifiers
Objectives
Understand how to use multivariate and Gaussian distributions to create bayes classifiers.
Prerequisites
Generated 2025-12-16T09:11.