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1. Trees & Graphs
Lecture-04

2. Contents
•ConventionalcomputingversusAI
•Tree and Graph Terminology
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3. •Conventional Computing:
•Conventional computing refers to traditional computer programs that are designed
and created by programmers to execute specific tasks.
•These programs consist of a series of well-defined instructions that the computer
follows in a linear manner.
•Think of it like a recipe in a cookbook -you follow the steps in order to get the desired
output.
ConventionalcomputingversusAI

4. Conventional computing…
1.Fixed Instructions:In conventional computing, the computer carries out
predetermined instructions. It can't change its behavior unless the programmer
explicitly alters the code.
2.Step-by-Step Execution:The computer follows a linear path of execution,
carrying out each instruction one after the other. It's like following a set of
directions.
3.Lack of Learning:Conventional programs don't learn from their experiences or
the data they process. They don't improve or adapt over time.
4.Human Intervention:If you want a conventional program to do something
new or different, you need to manually program it for that specific task.
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5. AI
•Artificial Intelligence (AI):
•Artificial Intelligence (AI), on the other hand, involves creating systems
that can learn from data and experiences, allowing them to make
decisions, recognize patterns, and adapt to new situations.
•AI systems don't just follow rigid instructions; they have the ability to
learn and think more like humans.
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6. AI….
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•Artificial Intelligence (AI):Artificial Intelligence (AI), on the other hand, involves
creating systems that can learn from data and experiences, allowing them to make
decisions, recognize patterns, and adapt to new situations. AI systems don't just follow
rigid instructions; they have the ability to learn and think more like humans. Here's
how AI differs:
1.Learning from Data:AI systems learn from large amounts of data. They can identify
patterns, correlations, and trends within the data to make informed decisions.
2.Decision-Making and Adaptation:Instead of fixed instructions, AI systems use
algorithms and models to make decisions. They can adapt their behavior based on the
data they process and the feedback they receive.
3.Autonomy and Self-Improvement:AI systems can improve over time without direct
human intervention. They learn from their mistakes and successes, refining their
performance.
4.Goal-Oriented:AI systems work towards achieving specific goals. They evaluate
different options and choose the one that best aligns with their objectives.

7. Artificial Intelligence VS Conventional Computing

8. Tree and Graph Terminology
•Root Node:The topmost node in a tree structure, serving as the starting point for traversal.
The root node serves as the starting point for decision-making or problem-solving in AI. It represents the initial state or question that needs
to be addressed.
•Parent Node:A node that has branches (children) stemming from it.
•Purpose in AI:Parent nodes in decision trees represent intermediate steps or conditions that help in making decisions. They guide the AI
system toward different paths based on the data.
•Child Node:A node connected to a parent node, forming branches of the tree.
•Purpose in AI:Child nodes are the outcomes or options that result from decisions made at parent nodes
•Leaf Node:A node with no children, located at the end of a branch.
•Purpose in AI:Leaf nodes in decision trees often represent final outcomes or classifications. They provide the results or conclusions of a
decision-making process.
•Internal Node:A node with at least one child.
•Depth and Height: depth refers to a node's distance from the root node, while height refers to the length of the longest path from a node
to a leaf node.
•Purpose in AI:The depth and height of a tree affect the complexity and efficiency of algorithms used in AI. A balanced tree can lead to
faster search and decision processes.
•Binary Tree:A tree in which each node has at most two children: left and right.
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9. depth
•Depth (Level):
•The depth of a node refers to its distance from the root node of the tree.
•The root node is considered to be at depth 0.
•Child nodes of the root node have a depth of 1, their children have a
depth of 2, and so on.
•Essentially, the depth of a node represents the number of edges in the
path from the root to that node
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10. Height
•The height of a node is the length of the longest path from that node to a
leaf node in the tree.
•Alternatively, the height of a node can be defined as the number of edges
on the longest downward path from that node to a leaf.
•Leaf nodes have a height of 0 because they don't have any children.
•The height of the entire tree is often defined as the height of the root
node, so it represents the length of the longest path from the root to any
leaf node in the tree.
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11. Tree and Graph Terminology
•Decision Tree:A type of tree used for decision-making, common in AI for
classification and regression tasks.
•Purpose in AI:Decision trees are a type of graph used in AI for
classification and regression tasks.
•They help in breaking down complex decisions into simpler steps based
on features or attributes of data.
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12. Tree and Graph Terminology
Tree Terminology:
A
B C
D E F G H
I J
“A, B, C …. J”are “nodes”
“A”is the “root node”
“B”is a “child”of “A”
“A”is ancestor of “D”
“D”is a descendant of “A”
“D, E, F, G, I, J”are “leaf nodes”
Arrows represent “edges”

13. Tree and Graph Terminology
Graph Terminology:
A
B C
D E F G H
I J
The search methods wewillbe dealing with are defined on trees and graphs

14. Graph Terminology
•Vertex (Node):A point in a graph that can hold data or attributes.
•Edge:A connection between two vertices, representing a relationship.
•Path:A sequence of vertices connected by edges in a graph.
•Cycle:A path that starts and ends at the same vertex, passing through
other vertices in between
•Shortest Path:The path between two vertices with the minimum sum of
edge weights/traversal
•Graph Traversal:The process of visiting all vertices in a graph, often used
to solve search and optimization problems..
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15. Graph Terminology
•Graph Purpose in AI:Graphs are used in AI for modeling
relationships and connections between data points.
•They are used in various algorithms, like representing knowledge
graphs or social networks.
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16. Thank You…!