What is ArtificialIntelligence (AI)?
• AI is the simulation of human intelligence in
machines.
• It enables machines to learn, reason, and
make decisions.
• Examples: Self-driving cars, chatbots, facial
recognition, etc.
3.
Understanding AI Further
•AI combines computer science, mathematics, and
cognitive science.
• It can be categorized as:
- Narrow AI: Performs specific tasks (e.g., Siri,
Google Translate)
- General AI: Performs any intellectual task like a
human (still theoretical)
• AI applications range across healthcare, finance,
robotics, and education.
4.
Techniques in AI
•Machine Learning (ML): Learning from data
• Deep Learning (DL): Neural networks with many
layers
• Natural Language Processing (NLP):
Understanding human language
• Computer Vision: Interpreting visual information
• Expert Systems: Rule-based decision-making
systems
5.
Example ML Algorithm:Decision
Trees
• Decision Trees are used for classification and
regression tasks.
• They split data based on feature values into
tree-like structures.
• Easy to interpret and visualize.
• Example: Classifying emails as spam or not
spam based on keywords.
6.
Types of MachineLearning
• Supervised Learning:
- Learns from labeled data
- Examples: Linear Regression, Decision Trees, SVM
• Unsupervised Learning:
- Works with unlabeled data
- Examples: K-Means Clustering, PCA
• Semi-Supervised Learning:
- Uses a small amount of labeled data with a large amount of unlabeled
data
• Reinforcement Learning:
- Agent learns by interacting with an environment and receiving rewards
- Example: Game-playing agents, robotics
7.
Practical Example: Supervised
Learningwith Decision Tree
Objective: Classify whether a person will buy a computer based on age and income.
Features:
• Age: Young, Middle-aged, Senior
• Income: High, Medium, Low
• Outcome: Buys Computer (Yes/No)
Decision Tree logic:
• IF Age = Young AND Income = High → No
• IF Age = Middle-aged → Yes
• IF Age = Senior AND Income = Medium → Yes
• This tree helps automate decision-making based on past labeled data.
