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<html><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
<title>neo4j display</title>
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/vis/4.21.0/vis.css" type="text/css" />
<script type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/vis/4.21.0/vis-network.min.js"> </script>
<style type="text/css">
#mynetwork {
width: 100%;
height: 500px;
}
</style>
</head>
<body data-gr-c-s-loaded="true">
<div id="mynetwork"><div class="vis-network" tabindex="900" style="position: relative; overflow: hidden; touch-action: pan-y; user-select: none; -webkit-user-drag: none; -webkit-tap-highlight-color: rgba(0, 0, 0, 0); width: 100%; height: 100%;"><canvas width="1200" height="800" style="position: relative; touch-action: none; user-select: none; -webkit-user-drag: none; -webkit-tap-highlight-color: rgba(0, 0, 0, 0); width: 100%; height: 100%;"></canvas></div></div>
<script type="text/javascript">
// create an array with nodes
var nodes = new vis.DataSet(
[{'id': 13, 'label': 'Problem solving'}, {'id': 51, 'label': 'Alpha Beta Pruning '}, {'id': 50, 'label': 'Min-Max Search'}, {'id': 49, 'label': 'Game Playing'}, {'id': 48, 'label': 'Adversarial Search'}, {'id': 47, 'label': 'Genetic algorithms'}, {'id': 46, 'label': 'Hill climbing search Simulated annealing'}, {'id': 45, 'label': 'Local Search Algorithms and Optimization Problems'}, {'id': 44, 'label': 'Memory bounded heuristic Search'}, {'id': 43, 'label': 'A* Search'}, {'id': 42, 'label': 'Greedy best first Search'}, {'id': 41, 'label': 'Informed Search Methods'}, {'id': 40, 'label': 'Depth First Iterative Deepening '}, {'id': 39, 'label': 'Depth Limited Search'}, {'id': 38, 'label': 'Depth First Search'}, {'id': 37, 'label': 'Breadth First Search'}, {'id': 36, 'label': 'Uninformed Search Methods'}, {'id': 11, 'label': 'Introduction to Artificial Intelligence'}, {'id': 25, 'label': 'Current trends in AI'}, {'id': 24, 'label': 'Applications of AI'}, {'id': 23, 'label': 'Sub-areas of AI'}, {'id': 22, 'label': 'Foundations of AI'}, {'id': 21, 'label': 'Components of AI Program'}, {'id': 20, 'label': 'Categorization of Intelligent System'}, {'id': 19, 'label': 'Intelligent Systems'}, {'id': 18, 'label': 'History of Artificial Intelligence'}, {'id': 17, 'label': 'Introduction'}, {'id': 74, 'label': 'PAC learning'}, {'id': 106, 'label': 'Generalization error'}, {'id': 132, 'label': 'Comparison with Boosting'}, {'id': 134, 'label': 'Constrained Optimization'}, {'id': 101, 'label': 'Types of Machine Learning'}, {'id': 34, 'label': 'Formulating Problems'}, {'id': 68, 'label': 'Planning with state space search'}, {'id': 78, 'label': 'Passive Reinforcement Learning,'}, {'id': 79, 'label': 'Active reinforcement Learning'}, {'id': 6, 'label': 'Clustering with minimal spanning tree'}, {'id': 33, 'label': 'Problem Solving Agent'}, {'id': 14, 'label': 'Knowledge and Reasoning'}, {'id': 66, 'label': 'Simple Inference in belief network'}, {'id': 65, 'label': 'semantics of belief network'}, {'id': 64, 'label': 'Representing knowledge in an uncertain domain'}, {'id': 63, 'label': 'Uncertainty'}, {'id': 62, 'label': 'Uncertain Knowledge and Reasoning'}, {'id': 61, 'label': 'Resolution'}, {'id': 60, 'label': 'Unification'}, {'id': 59, 'label': 'Knowledge Engineering in First-Order Logic'}, {'id': 58, 'label': 'backward Chaining'}, {'id': 57, 'label': 'Forward chaining'}, {'id': 56, 'label': 'Inference in FOL'}, {'id': 55, 'label': 'Syntax and Semantic'}, {'id': 54, 'label': 'First Order Logic'}, {'id': 53, 'label': 'Brief Overview of propositional logic'}, {'id': 52, 'label': 'Knowledge based Agents'}, {'id': 107, 'label': 'Overfitting'}, {'id': 118, 'label': 'Sensitivity'}, {'id': 111, 'label': 'Multivariate Linear Regression'}, {'id': 88, 'label': 'Applications in Healthcare'}, {'id': 131, 'label': 'Random Forest'}, {'id': 5, 'label': 'Major clustering approaches'}, {'id': 108, 'label': 'Underfitting'}, {'id': 87, 'label': 'Problems Robotics can solve'}, {'id': 83, 'label': 'Parsing'}, {'id': 75, 'label': 'Introduction to statistical learning'}, {'id': 126, 'label': 'Boosting'}, {'id': 119, 'label': 'Specificity'}, {'id': 86, 'label': 'Robot hardware'}, {'id': 121, 'label': 'Recall'}, {'id': 102, 'label': 'Issues in Machine Learning'}, {'id': 117, 'label': '[Kappa Statistics]'}, {'id': 114, 'label': 'Decision Trees using Gini Index'}, {'id': 133, 'label': 'Different ways to combine classifiers'}, {'id': 139, 'label': 'SVM for linear and nonlinear classification'}, {'id': 124, 'label': 'Understanding Ensembles'}, {'id': 125, 'label': 'K-fold cross validation'}, {'id': 97, 'label': 'Ensemble Learning'}, {'id': 130, 'label': 'Subagging'}, {'id': 129, 'label': 'Bagging'}, {'id': 128, 'label': 'XGBoost'}, {'id': 127, 'label': 'Stumping'}, {'id': 16, 'label': 'AI Applications'}, {'id': 90, 'label': 'Banking'}, {'id': 89, 'label': 'Retail'}, {'id': 85, 'label': 'Robots'}, {'id': 84, 'label': 'Robotics'}, {'id': 82, 'label': 'Grammars'}, {'id': 81, 'label': 'Language models'}, {'id': 80, 'label': ' Introduction to NLP'}, {'id': 113, 'label': 'Decision Trees'}, {'id': 28, 'label': 'Nature of environment'}, {'id': 109, 'label': 'Bias-Variance Trade Off'}, {'id': 135, 'label': 'Optimal decision boundary'}, {'id': 96, 'label': 'Learning with Regression and Trees'}, {'id': 123, 'label': 'ROC curve'}, {'id': 122, 'label': 'F-measure'}, {'id': 120, 'label': 'Precision'}, {'id': 116, 'label': 'Confusion Matrix'}, {'id': 115, 'label': 'Classification and Regression Trees'}, {'id': 112, 'label': 'Logistic Regression'}, {'id': 110, 'label': 'Linear Regression'}, {'id': 73, 'label': 'Theory of Learning'}, {'id': 141, 'label': 'Principal Component Analysis'}, {'id': 31, 'label': 'Learning Agent'}, {'id': 30, 'label': 'Types of Agents'}, {'id': 29, 'label': 'Structure of Agents'}, {'id': 76, 'label': 'Introduction to reinforcement learning'}, {'id': 26, 'label': 'Agents and Environments'}, {'id': 98, 'label': 'Learning with Classification'}, {'id': 2, 'label': 'Multiclass Classification'}, {'id': 1, 'label': 'Support Vector Regression'}, {'id': 0, 'label': 'Basics of Kernel trick'}, {'id': 138, 'label': 'Quadratic Programming'}, {'id': 137, 'label': 'SVM as constrained optimization problem'}, {'id': 136, 'label': 'Margins and support vectors'}, {'id': 100, 'label': 'Dimensionality Reduction '}, {'id': 9, 'label': 'Singular Valued Decomposition'}, {'id': 142, 'label': 'Linear Discriminant Analysis'}, {'id': 140, 'label': 'Dimensionality Reduction Techniques'}, {'id': 8, 'label': 'Density Based Clustering: DBSCAN'}, {'id': 104, 'label': 'Steps in developing a ML Application'}, {'id': 70, 'label': 'Hierarchical planning'}, {'id': 103, 'label': 'Application of Machine Learning'}, {'id': 35, 'label': 'Example Problems'}, {'id': 12, 'label': 'Intelligent Agents'}, {'id': 32, 'label': 'Solving problem by Searching'}, {'id': 27, 'label': 'Concept of rationality'}, {'id': 105, 'label': 'Training Error'}, {'id': 71, 'label': 'Conditional Planning'}, {'id': 7, 'label': 'Expectation Maximization Algorithm'}, {'id': 69, 'label': 'Partial order planning'}]
);
// create an array with edges
var edges = new vis.DataSet(
[{'from': 13, 'to': 51, 'label': 'has'}, {'from': 13, 'to': 50, 'label': 'has'}, {'from': 13, 'to': 49, 'label': 'has'}, {'from': 13, 'to': 48, 'label': 'has'}, {'from': 13, 'to': 47, 'label': 'has'}, {'from': 13, 'to': 46, 'label': 'has'}, {'from': 13, 'to': 45, 'label': 'has'}, {'from': 13, 'to': 44, 'label': 'has'}, {'from': 13, 'to': 43, 'label': 'has'}, {'from': 13, 'to': 42, 'label': 'has'}, {'from': 13, 'to': 41, 'label': 'has'}, {'from': 13, 'to': 40, 'label': 'has'}, {'from': 13, 'to': 39, 'label': 'has'}, {'from': 13, 'to': 38, 'label': 'has'}, {'from': 13, 'to': 37, 'label': 'has'}, {'from': 13, 'to': 36, 'label': 'has'}, {'from': 11, 'to': 25, 'label': 'has'}, {'from': 11, 'to': 24, 'label': 'has'}, {'from': 11, 'to': 23, 'label': 'has'}, {'from': 11, 'to': 22, 'label': 'has'}, {'from': 11, 'to': 21, 'label': 'has'}, {'from': 11, 'to': 20, 'label': 'has'}, {'from': 11, 'to': 19, 'label': 'has'}, {'from': 11, 'to': 18, 'label': 'has'}, {'from': 11, 'to': 17, 'label': 'has'}, {'from': 14, 'to': 66, 'label': 'has'}, {'from': 14, 'to': 65, 'label': 'has'}, {'from': 14, 'to': 64, 'label': 'has'}, {'from': 14, 'to': 63, 'label': 'has'}, {'from': 14, 'to': 62, 'label': 'has'}, {'from': 14, 'to': 61, 'label': 'has'}, {'from': 14, 'to': 60, 'label': 'has'}, {'from': 14, 'to': 59, 'label': 'has'}, {'from': 14, 'to': 58, 'label': 'has'}, {'from': 14, 'to': 57, 'label': 'has'}, {'from': 14, 'to': 56, 'label': 'has'}, {'from': 14, 'to': 55, 'label': 'has'}, {'from': 14, 'to': 54, 'label': 'has'}, {'from': 14, 'to': 53, 'label': 'has'}, {'from': 14, 'to': 52, 'label': 'has'}, {'from': 97, 'to': 133, 'label': 'has'}, {'from': 97, 'to': 132, 'label': 'has'}, {'from': 97, 'to': 131, 'label': 'has'}, {'from': 97, 'to': 130, 'label': 'has'}, {'from': 97, 'to': 129, 'label': 'has'}, {'from': 97, 'to': 128, 'label': 'has'}, {'from': 97, 'to': 127, 'label': 'has'}, {'from': 97, 'to': 126, 'label': 'has'}, {'from': 97, 'to': 125, 'label': 'has'}, {'from': 97, 'to': 124, 'label': 'has'}, {'from': 16, 'to': 90, 'label': 'has'}, {'from': 16, 'to': 89, 'label': 'has'}, {'from': 16, 'to': 88, 'label': 'has'}, {'from': 16, 'to': 87, 'label': 'has'}, {'from': 16, 'to': 86, 'label': 'has'}, {'from': 16, 'to': 85, 'label': 'has'}, {'from': 16, 'to': 84, 'label': 'has'}, {'from': 16, 'to': 83, 'label': 'has'}, {'from': 16, 'to': 82, 'label': 'has'}, {'from': 16, 'to': 81, 'label': 'has'}, {'from': 16, 'to': 80, 'label': 'has'}, {'from': 96, 'to': 123, 'label': 'has'}, {'from': 96, 'to': 122, 'label': 'has'}, {'from': 96, 'to': 121, 'label': 'has'}, {'from': 96, 'to': 120, 'label': 'has'}, {'from': 96, 'to': 119, 'label': 'has'}, {'from': 96, 'to': 118, 'label': 'has'}, {'from': 96, 'to': 117, 'label': 'has'}, {'from': 96, 'to': 116, 'label': 'has'}, {'from': 96, 'to': 115, 'label': 'has'}, {'from': 96, 'to': 114, 'label': 'has'}, {'from': 96, 'to': 113, 'label': 'has'}, {'from': 96, 'to': 112, 'label': 'has'}, {'from': 96, 'to': 111, 'label': 'has'}, {'from': 96, 'to': 110, 'label': 'has'}, {'from': 98, 'to': 2, 'label': 'has'}, {'from': 98, 'to': 1, 'label': 'has'}, {'from': 98, 'to': 0, 'label': 'has'}, {'from': 98, 'to': 139, 'label': 'has'}, {'from': 98, 'to': 138, 'label': 'has'}, {'from': 98, 'to': 137, 'label': 'has'}, {'from': 98, 'to': 136, 'label': 'has'}, {'from': 98, 'to': 135, 'label': 'has'}, {'from': 98, 'to': 134, 'label': 'has'}, {'from': 100, 'to': 9, 'label': 'has'}, {'from': 100, 'to': 142, 'label': 'has'}, {'from': 100, 'to': 141, 'label': 'has'}, {'from': 100, 'to': 140, 'label': 'has'}, {'from': 12, 'to': 35, 'label': 'has'}, {'from': 12, 'to': 34, 'label': 'has'}, {'from': 12, 'to': 33, 'label': 'has'}, {'from': 12, 'to': 32, 'label': 'has'}, {'from': 12, 'to': 31, 'label': 'has'}, {'from': 12, 'to': 30, 'label': 'has'}, {'from': 12, 'to': 29, 'label': 'has'}, {'from': 12, 'to': 28, 'label': 'has'}, {'from': 12, 'to': 27, 'label': 'has'}, {'from': 12, 'to': 26, 'label': 'has'}]
);
// create a network
var container = document.getElementById('mynetwork');
var data = {
nodes: nodes,
edges: edges
};
var options = {
nodes: {
font: { color: 'white',size: 14 },
color: '#F9A6C1',
size: 25,
shape: 'circle',
widthConstraint: 60
},
edges : {
arrows: {
to: {enabled: true, scaleFactor: 0.5}
},
"color": {
"inherit": false
},
font: {size: 14, align: 'middle'},
"smooth": {
"enabled": true,
"type": "dynamic",
},
"length": 200
},
"interaction": {
"dragNodes": true,
"hideEdgesOnDrag": false,
"hideNodesOnDrag": false
},
"physics": {
"enabled": true,
"stabilization": {
"enabled": true,
"fit": true,
}
}
};
var network = new vis.Network(container, data, options);
</script>
</body></html>