Computational Thinking and Python Programming

Study Notes

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

Description

Test your understanding of computational thinking concepts such as abstraction, decomposition, and pattern recognition. Learn about algorithms, flowcharts, and Python programming concepts like sequence, selection, and iterations.