Computer Systems Basics

Computer Systems

• A computer is a programmable electronic device that can store, process, and communicate information.
• Fixed computers perform a specific task and cannot be changed, whereas programmable computers can be programmed to perform different tasks.
• Calculators are specialized computers designed for mathematical calculations, whereas computers are general-purpose devices.
• Input devices: keyboard, mouse, scanner, etc.
• Output devices: monitor, printer, speaker, etc.

RAM vs ROM

• RAM (Random Access Memory) is a volatile memory that temporarily stores data and program instructions, allowing the CPU to access and process them quickly.
• ROM (Read-Only Memory) is a non-volatile memory that permanently stores data and program instructions, which cannot be changed by the user.

Secondary Storage

• Secondary storage is necessary to store large amounts of data permanently, even when the power is turned off.
• Types of secondary storage: Hard Disk Drive (HDD), Solid-State Drive (SSD), Flash Drive, CD, DVD, Blu-ray.
• Magnetic technology uses magnetic fields to store data on disks, optical technology uses light to store data on discs, and solid-state technology uses flash memory to store data.

CPU

• The CPU (Central Processing Unit) is the brain of the computer, executing instructions and performing calculations.
• Parts of the CPU: ALU (Arithmetic Logic Unit), Busses, Clock, Registers, Control Unit.

Operating System

• The operating system manages computer resources, providing a platform for running application software.
• Roles of the operating system: Managing Memory, Program execution, Managing Input and Output, Managing User Interface (GUI), Managing Communication.

Binary

• Binary is a number system that uses only two digits: 0 and 1.
• To convert Denary to Binary, divide the number by 2 and note the remainders.
• To convert Binary to Denary, multiply each digit by the corresponding power of 2 and add them up.

Data Sizes

• Bit: the smallest unit of data, representing a single binary digit.
• Nibble: a group of 4 bits.
• Byte: a group of 8 bits, representing a single character.
• Kilobyte (KB): 1024 bytes.
• Megabyte (MB): 1024 KB.
• Gigabyte (GB): 1024 MB.
• Terabyte (TB): 1024 GB.

Algorithms, Computational Thinking, and Introduction to Python

• Computational Thinking: a problem-solving approach that involves abstraction, decomposition, pattern recognition, and algorithmic thinking.
• Abstraction: focusing on essential features while ignoring irrelevant details.
• Decomposition: breaking down complex problems into smaller, manageable parts.
• Pattern recognition: identifying patterns and relationships in data.
• Algorithm: a step-by-step procedure for solving a problem.

Representing Algorithms

• Flowcharts: a graphical representation of an algorithm using shapes and symbols.
• Correct use of shapes in flowcharts: rectangles for processes, diamonds for decisions, and triangles for input/output.
• Creating a flowchart for an algorithm: identify inputs, processes, and outputs.

Python Programming

• Sequence: the order of steps in a program or algorithm.
• Selection: using if-else statements to make decisions based on conditions.
• Iteration: repeating a sequence of steps using while loops or for loops.
• Variables: storing and changing values in a program.
• Data Types: integers, floats, strings, booleans, etc.
• Casting: converting data types during input.

AI Experience

• AI (Artificial Intelligence): the development of computer systems that can perform tasks that typically require human intelligence.
• Types of AI: Machine Learning, Narrow AI, General AI.
• Machine Learning: a type of AI that enables computers to learn from data without being explicitly programmed.
• Types of machine learning: Supervised, Unsupervised, Reinforcement, Semi-supervised.

AI Lifecycle

• Defining the problem: identifying a problem or opportunity for AI to solve.
• Preparing Data: collecting, cleaning, and preprocessing data for machine learning.
• Training: using machine learning algorithms to train a model on prepared data.
• Testing: evaluating the performance of the trained model on new data.
• Evaluating the Model: measuring the accuracy and confidence of the model.

Machine Learning: Data Preparation (Cleaning)

• Duplicates: removing duplicate data points to prevent bias.
• Missing data: handling missing values to prevent errors.
• Invalid data: detecting and removing invalid or corrupted data.

Machine Learning: Testing

• Testing for Bias: evaluating the model for bias and fairness.
• Measuring accuracy and confidence: evaluating the model's performance using metrics such as precision, recall, and F1 score.

Decision Trees

• Decision trees: a machine learning model that uses a tree-like structure to classify data.
• How decision trees are made: recursively partitioning data into smaller subsets based on features.
• Solving problems with ML models: using decision trees to solve classification problems.

Computer Systems

• A computer is a programmable electronic device that can store, process, and communicate information.
• Fixed computers perform a specific task and cannot be changed, whereas programmable computers can be programmed to perform different tasks.
• Calculators are specialized computers designed for mathematical calculations, whereas computers are general-purpose devices.
• Input devices: keyboard, mouse, scanner, etc.
• Output devices: monitor, printer, speaker, etc.

RAM vs ROM

• RAM (Random Access Memory) is a volatile memory that temporarily stores data and program instructions, allowing the CPU to access and process them quickly.
• ROM (Read-Only Memory) is a non-volatile memory that permanently stores data and program instructions, which cannot be changed by the user.

Secondary Storage

• Secondary storage is necessary to store large amounts of data permanently, even when the power is turned off.
• Types of secondary storage: Hard Disk Drive (HDD), Solid-State Drive (SSD), Flash Drive, CD, DVD, Blu-ray.
• Magnetic technology uses magnetic fields to store data on disks, optical technology uses light to store data on discs, and solid-state technology uses flash memory to store data.

CPU

• The CPU (Central Processing Unit) is the brain of the computer, executing instructions and performing calculations.
• Parts of the CPU: ALU (Arithmetic Logic Unit), Busses, Clock, Registers, Control Unit.

Operating System

• The operating system manages computer resources, providing a platform for running application software.
• Roles of the operating system: Managing Memory, Program execution, Managing Input and Output, Managing User Interface (GUI), Managing Communication.

Binary

• Binary is a number system that uses only two digits: 0 and 1.
• To convert Denary to Binary, divide the number by 2 and note the remainders.
• To convert Binary to Denary, multiply each digit by the corresponding power of 2 and add them up.

Data Sizes

• Bit: the smallest unit of data, representing a single binary digit.
• Nibble: a group of 4 bits.
• Byte: a group of 8 bits, representing a single character.
• Kilobyte (KB): 1024 bytes.
• Megabyte (MB): 1024 KB.
• Gigabyte (GB): 1024 MB.
• Terabyte (TB): 1024 GB.

Algorithms, Computational Thinking, and Introduction to Python

• Computational Thinking: a problem-solving approach that involves abstraction, decomposition, pattern recognition, and algorithmic thinking.
• Abstraction: focusing on essential features while ignoring irrelevant details.
• Decomposition: breaking down complex problems into smaller, manageable parts.
• Pattern recognition: identifying patterns and relationships in data.
• Algorithm: a step-by-step procedure for solving a problem.

Representing Algorithms

• Flowcharts: a graphical representation of an algorithm using shapes and symbols.
• Correct use of shapes in flowcharts: rectangles for processes, diamonds for decisions, and triangles for input/output.
• Creating a flowchart for an algorithm: identify inputs, processes, and outputs.

Python Programming

• Sequence: the order of steps in a program or algorithm.
• Selection: using if-else statements to make decisions based on conditions.
• Iteration: repeating a sequence of steps using while loops or for loops.
• Variables: storing and changing values in a program.
• Data Types: integers, floats, strings, booleans, etc.
• Casting: converting data types during input.

AI Experience

• AI (Artificial Intelligence): the development of computer systems that can perform tasks that typically require human intelligence.
• Types of AI: Machine Learning, Narrow AI, General AI.
• Machine Learning: a type of AI that enables computers to learn from data without being explicitly programmed.
• Types of machine learning: Supervised, Unsupervised, Reinforcement, Semi-supervised.

AI Lifecycle

• Defining the problem: identifying a problem or opportunity for AI to solve.
• Preparing Data: collecting, cleaning, and preprocessing data for machine learning.
• Training: using machine learning algorithms to train a model on prepared data.
• Testing: evaluating the performance of the trained model on new data.
• Evaluating the Model: measuring the accuracy and confidence of the model.

Machine Learning: Data Preparation (Cleaning)

• Duplicates: removing duplicate data points to prevent bias.
• Missing data: handling missing values to prevent errors.
• Invalid data: detecting and removing invalid or corrupted data.

Machine Learning: Testing

• Testing for Bias: evaluating the model for bias and fairness.
• Measuring accuracy and confidence: evaluating the model's performance using metrics such as precision, recall, and F1 score.

Decision Trees

• Decision trees: a machine learning model that uses a tree-like structure to classify data.
• How decision trees are made: recursively partitioning data into smaller subsets based on features.
• Solving problems with ML models: using decision trees to solve classification problems.