Computer Systems Fundamentals

Computer Systems

• A computer is a programmable electronic device that can store, process, and communicate information.
• Fixed computers are designed to perform a specific task, while programmable computers can be instructed to perform different tasks.
• Calculators are limited to performing mathematical calculations, whereas computers can perform a wide range of tasks.

Input and Output

• Input devices: keyboard, mouse, scanner, microphone, 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 while the computer is running.
• ROM (Read-Only Memory) is a non-volatile memory that permanently stores data and program instructions.

Secondary Storage

• Secondary storage is necessary to store large amounts of data permanently.
• Types of secondary storage: Hard Disk Drive (HDD), Solid-State Drive (SSD), Flash Drive, etc.
• Technologies used: magnetic, optical, solid-state.

CPU

• CPU (Central Processing Unit) is the brain of the computer that executes program instructions.
• Parts of a CPU:
  • ALU (Arithmetic Logic Unit): performs arithmetic and logical operations.
  • Busses: connects different components of the CPU.
  • Clock: synchronizes the CPU's operations.
  • Registers: small amount of memory built into the CPU.
  • Control Unit: retrieves and executes program instructions.

Operating System

• Role of the operating system:
  • Managing Memory: allocates and deallocates memory for programs.
  • Program execution: executes program instructions.
  • Managing Input and Output: controls input and output operations.
  • Managing User Interface (GUI): provides a graphical interface for users.
  • Managing Communication: facilitates communication between devices.

Binary

• Binary is a number system that uses only two digits: 0 and 1.
• Binary conversion: Denary to Binary and Binary to Denary.
• Binary maths: addition and subtraction of binary numbers.

Data Sizes

• Bit: smallest unit of data (0 or 1).
• Nibble: 4 bits.
• Byte: 8 bits.
• Kilobyte (KB): 1024 bytes.
• Megabyte (MB): 1024 KB.
• Gigabyte (GB): 1024 MB.
• Terabyte (TB): 1024 GB.

Computational Thinking

• Abstraction: breaking down complex problems into smaller, manageable parts.
• Decomposition: breaking down problems into smaller, more manageable parts.
• Pattern recognition: identifying patterns and relationships in data.

Algorithms

• Algorithm: a step-by-step procedure for solving a problem.
• Representing algorithms: using flowcharts, pseudocode, and programming languages.
• Flowchart: a diagram that represents the steps of an algorithm.
• Identifying inputs, outputs, and processing in an algorithm.

Python Programming

• Sequence: executing a series of steps in a program.
• Selection: making decisions based on conditions in a program.
• Iterations: repeating a set of steps in a program.
• Variables: storing and using values in a program.
• Assigning and changing the values of variables.
• Selection: using if-else statements.
• Data types: understanding the main data types (integer, float, string, etc.).
• Casting data types: converting data types during input.

Iteration

• Using While loops and For loops in a program.
• Understanding what to include in a loop.

AI

• Rule-based programming: using predefined rules to solve a problem.
• Data-driven programming: using data to train a model to solve a problem.
• Types of AI:
  • Machine Learning
  • Narrow AI
  • General AI
• Types of machine learning:
  • Supervised
  • Unsupervised
  • Reinforcement
  • Semi-supervised

AI Lifecycle

• Defining the problem: identifying the problem to be solved.
• Preparing data: collecting and cleaning data.
• Training: training the model using the prepared data.
• Testing: evaluating the model's performance.
• Evaluating the model: measuring the model's accuracy and confidence.

Machine Learning: Data Preparation

• Cleaning data: handling duplicates, missing data, and invalid data.

Machine Learning: Testing

• Testing for bias: identifying biases in the model.
• Measuring accuracy and confidence: evaluating the model's performance.

Decision Trees

• Creating decision trees: a step-by-step process for making decisions.

Breadboards

• Understanding the arrangement and effect of placing connections on bars and rails of a breadboard.
• Identifying correct pin to breadboard numbers using a diagram.