Computational Thinking and Python Programming

Computational Thinking

• Abstraction: Focus on essential features of a problem, ignoring irrelevant details
• Decomposition: Break down complex problems into smaller, manageable parts
• Pattern recognition: Identify relationships and patterns in data

Algorithms

• Definition: A set of instructions to solve a problem or perform a task
• Representation: Flowcharts, pseudocode, or programming languages
• Components:
  • Input: Data or information provided to the algorithm
  • Process: Steps that transform input into output
  • Output: Result of the algorithm's processing

Python Programming

Sequence

• A series of steps executed in a specific order

Selection

• If-else statements used to make decisions in a program
• Conditional statements that direct the program's flow

Iterations

• Loops (while, for) used to repeat tasks
• Repeated execution of a block of code

Variables

• Store and manipulate data in a program
• Assign, change, and reuse values

Data Types

• Types of data that can be stored in a variable
• Examples: integers, floating-point numbers, strings, booleans
• Casting: Converting data from one type to another

Artificial Intelligence (AI)

• Rule-based programming: Logical rules govern behavior
• Data-driven programming: Behavior based on patterns in data

Types of AI

• Machine Learning (ML)
• Narrow AI (task-specific)
• General AI (human-like intelligence)

Machine Learning

• Supervised learning: Labeled data guides the model
• Unsupervised learning: Unlabeled data reveals patterns
• Reinforcement learning: Feedback guides the model
• Semi-supervised learning: Combines labeled and unlabeled data

AI Lifecycle

• Defining the problem: Identify the issue to be solved
• Preparing Data: Collect, clean, and preprocess data
• Training: Teach the model using prepared data
• Testing: Evaluate the model's performance
• Evaluating the Model: Assess its accuracy and reliability

Machine Learning: Data Preparation

• Handling issues:
  • Duplicates: Removing duplicate data points
  • Missing data: Filling in gaps or ignoring missing values
  • Invalid data: Correcting or removing incorrect data

Machine Learning: Testing

• Testing for bias: Ensuring fair and unbiased models
• Measuring accuracy and confidence: Evaluating model performance
• Bias in, bias out: Avoiding perpetuating biases in models

Decision Trees

• A type of ML model for classification and regression tasks
• Construction: Recursive partitioning of data

Solving Problems with ML Models

• Using decision trees and other ML models to address real-world problems