Bayesian Networks: Software | Saylor Academy

Software

Notable software for Bayesian networks include:

Just another Gibbs sampler (JAGS) – Open-source alternative to WinBUGS. Uses Gibbs sampling.
OpenBUGS – Open-source development of WinBUGS.
SPSS Modeler – Commercial software that includes an implementation for Bayesian networks.
Stan (software) – Stan is an open-source package for obtaining Bayesian inference using the No-U-Turn sampler (NUTS), a variant of Hamiltonian Monte Carlo.
PyMC3 – A Python library implementing an embedded domain specific language to represent bayesian networks, and a variety of samplers (including NUTS)
WinBUGS – One of the first computational implementations of MCMC samplers. No longer maintained.

Course Introduction

Course Syllabus

Unit 1: What Is Artificial Intelligence?

1.1: The Turing Test

The Turing Test for Intelligence

Why the Turing Test Is Important

1.2: The Four Types of AI

Is Intelligence How You Think or the Output of Thinking?

Unit 1 Assessment

Unit 1 Assessment

Unit 2: Agent-Based Approach to AI

2.1: Introduction to Agent-Based AI

Agents, Agent Types, and Their Capabilities

2.2: Analyzing Environmental Characteristics

Properties of Problem Environments and How to Analyze Them

Unit 2 Assessment

Unit 2 Assessment

Unit 3: Machine Learning and Its Importance

3.1: Learning in AI and Agents

Supervised, Unsupervised, and Reinforcement ML

3.2: Applications of ML in Neural Networks

Newer Machine Learning Models and Applications

Unit 3 Assessment

Unit 3 Assessment

Unit 4: Machine Learning Algorithms

4.1: Classification Algorithms

Classification versus Regression

Importance of Classification and Regression in Machine Learning

Classification Using K-nearest Neighbors Algorithm

4.2: Classification Algorithm Performance

False Positives / False Negatives / Confusion Matrix

Precision and Recall Calculations from Confusion Matrix

Linear Regression – How It Works

4.3: Linear Regression Algorithms

Metrics for Linear Regression Effectiveness: R-squared, MSE and RSE

Lasso and Ridge Regression

Improving Linear Regression by Reducing Residual Errors

4.4: Other Supervised ML Classification Algorithms

Classification Using Decision Trees

Classification Using Logistic Regression

Applying Bayes' Theorem in Machine Learning

4.5: Unsupervised Learning and Reinforcement Learning

Unlabelled Data and Unsupervised Machine Learning

Principles and Applications of Reinforcement Learning

4.6: ML Using Neural Networks

Introduction to Neural Networks Basics

Neural Networks: Types and Applications

Unit 4 Assessment

Unit 4 Assessment

Unit 5: Problem-Solving Methods in AI

5.1: Integrating ML Skills

Applying Classification to Determine Insurability

How Regression Is Applied in Contemporary Computing

Using Neural Networks in Cancer Detection

5.2: General AI Problem-Solver Architecture

Characteristics of General Problem-Solver

5.3: Designing a General Problem-Solving Agent

How GPS Is Used

Computational Tractability of GPS

Unit 5 Assessment

Unit 5 Assessment

Unit 6: Search Algorithms

6.1: Uninformed Search Algorithms

Uninformed or Brute Force Search

Depth First Search Algorithm

Breadth First Search Algorithm

Uniform Cost Search Algorithm

6.2: Heuristic Search Algorithms

Heuristics and Using Them to Improve Search

Overview of A* Search and Analysis of Performance

Unit 6 Assessment

Unit 6 Assessment

Unit 7: Iterative Improvement Algorithms

7.1: Using Iterative Improvement to Solve Problems

Iterative Improvement Algorithms and Hill-Climbing

Constraint Satisfaction Problems and Their Importance

7.2: Improving Algorithm Efficiency

How Simulated Annealing Improves Hill-Climbing

Improving Mediocre Solutions Using Genetic Algorithms

Unit 7 Assessment