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21:5121:51, 9 July 2025 4.2 Algorithms (hist | edit) [535 bytes] Mr. Goldstein (talk | contribs) (Created page with "== 4.2 Algorithms == Imagine you have a recipe for baking cookies. That recipe tells you exactly what ingredients to use and what steps to follow, in order, to get your delicious cookies. In computer science, an '''algorithm''' is very similar! It's a set of clear, step-by-step instructions that a computer can follow to solve a problem or complete a task. Algorithms are super important because they are the "brains" behind all the apps, games, and websites you use every...") Tag: Visual edit
21:3421:34, 9 July 2025 5.5 Assembly Language (Basic Concepts) (hist | edit) [3,241 bytes] Mr. Goldstein (talk | contribs) (Created page with "== 5.5 Assembly Language (Basic Concepts) == You know how computers understand instructions in their very own "instruction set" (those tiny commands like "add," "move," "store")? Well, these instructions are actually stored inside the computer as long strings of 0s and 1s, called '''machine code'''. Trying to program directly in 0s and 1s would be incredibly difficult for a human! Imagine trying to write an essay only using dots and dashes. That's where '''Assembly Lang...") Tag: Visual edit
20:4920:49, 9 July 2025 5.4.1 RISC vs. CISC (hist | edit) [3,440 bytes] Mr. Goldstein (talk | contribs) (Created page with "=== 5.4.1 RISC vs. CISC === When computer engineers design a CPU's instruction set, they usually follow one of two main philosophies: RISC or CISC. It's like deciding if our robot should have a few very simple commands or many complicated ones. # '''RISC (Reduced Instruction Set Computer):''' #* '''Idea:''' Think of this like a chef who has many simple, quick tools (like a knife, a spoon, a whisk). Each tool does one basic job very fast. To make a fancy meal, the chef c...") Tag: Visual edit
20:1720:17, 9 July 2025 5.4 Instruction Sets (hist | edit) [1,626 bytes] Mr. Goldstein (talk | contribs) (Created page with "== 5.4 Instruction Sets == Imagine you're trying to give commands to a robot. If the robot only understands simple commands like "move forward," "turn left," "pick up," and "drop," that's its basic set of instructions. It can't understand "make me a sandwich" as one command; you'd have to break that down into many tiny steps. In a similar way, a computer's CPU (its brain) doesn't understand high-level programming languages like Python or Scratch directly. Instead, it un...") Tag: Visual edit
20:0120:01, 9 July 2025 5.3.2 Polling, Interrupts, DMA (hist | edit) [3,545 bytes] Mr. Goldstein (talk | contribs) (Created page with "=== 5.3.2 Polling, Interrupts, DMA === The CPU is incredibly fast and busy, so it needs smart ways to communicate with I/O devices without wasting too much time. Imagine the CPU is a very important manager, and I/O devices are employees who sometimes need the manager's attention. There are a few different ways this communication can happen: # '''Polling:''' #* '''How it works:''' This is like the manager (CPU) constantly asking ''each'' employee (I/O device) one by one,...") Tag: Visual edit
19:1519:15, 9 July 2025 5.3.1 I/O Devices and Controllers (hist | edit) [2,599 bytes] Mr. Goldstein (talk | contribs) (Created page with "=== 5.3.1 I/O Devices and Controllers === When we talk about Input/Output, we're really talking about two main things: the devices themselves and the special helpers that let them talk to the CPU. '''I/O Devices''' are the physical gadgets you use to put information into your computer or get information out. You use them every day! * '''Input Devices:''' ** '''Keyboard:''' For typing words and numbers. ** '''Mouse/Trackpad:''' For pointing, clicking, and moving things...") Tag: Visual edit
19:1319:13, 9 July 2025 5.3 Input/Output Systems (hist | edit) [2,575 bytes] Mr. Goldstein (talk | contribs) (Created page with "== 5.3 Input/Output Systems == Imagine your computer is a person who needs to talk to the world and take in information. How does it do that? It uses '''Input/Output (I/O) Systems'''. "Input" is how information and commands get ''into'' the computer. Think about typing on a keyboard, clicking a mouse, or using a microphone to record your voice. All of these are ways you give input to the computer. "Output" is how the computer gives information ''back'' to you or to othe...") Tag: Visual edit
18:4318:43, 9 July 2025 5.2.4 Virtual Memory (Basic Concept) (hist | edit) [2,446 bytes] Mr. Goldstein (talk | contribs) (Created page with "=== 5.2.4 Virtual Memory (Basic Concept) === Imagine your computer has a certain amount of RAM (its "working desk"). What happens if you try to open so many programs or files that they all can't fit on that desk at the same time? Your computer doesn't just crash! Instead, it uses a clever trick called '''Virtual Memory'''. Virtual Memory is like your computer "borrowing" some extra space from your long-term storage device (usually your hard drive or SSD, which is normal...") Tag: Visual edit
18:2918:29, 9 July 2025 5.2.3 ROM (Read-Only Memory) (hist | edit) [2,450 bytes] Mr. Goldstein (talk | contribs) (Created page with "=== 5.2.3 ROM (Read-Only Memory) === While RAM is like your computer's temporary desk where information comes and goes, '''ROM''' (which stands for '''Read-Only Memory''') is more like a permanent instruction manual or a set of rules engraved into the computer's foundation. As its name suggests, "Read-Only" means that the computer can read information from it, but it generally cannot easily write new information to it or change what's already there. Here's why ROM is so...") Tag: Visual edit
18:2618:26, 9 July 2025 5.2.2 RAM (Random Access Memory) (hist | edit) [2,265 bytes] Mr. Goldstein (talk | contribs) (Created page with "=== 5.2.2 RAM (Random Access Memory) === '''RAM''' (pronounced like "ram") stands for '''Random Access Memory''', and it's one of the most important types of memory in your computer. You can think of RAM as your computer's main "working desk" or "short-term memory." When you open a program, surf the internet, edit a document, or play a game, all the information that the computer needs to actively work with right now is loaded into RAM. Why is it called "Random Access"?...") Tag: Visual edit
16:2216:22, 9 July 2025 5.2.1 Cache Memory (L1, L2, L3) (hist | edit) [2,451 bytes] Mr. Goldstein (talk | contribs) (Created page with "=== 5.2.1 Cache Memory (L1, L2, L3) === '''Cache memory''' is like the CPU's personal, super-fast mini-notepad. It's tiny but incredibly quick. The CPU stores data it uses very often in cache, so it doesn't have to go all the way to the main memory, which is slower. There are usually different levels of cache: * '''L1 Cache:''' This is the fastest and smallest cache, right inside the CPU. * '''L2 Cache:''' A bit larger and slightly slower than L1, but still very fast. *...") Tag: Visual edit
16:1516:15, 9 July 2025 5.2 Memory Hierarchy (hist | edit) [1,665 bytes] Mr. Goldstein (talk | contribs) (Created page with "== 5.2 Memory Hierarchy == Computers need places to store information, and they have different types of storage, just like you might have a tiny notepad for quick notes, a backpack for school books, and a whole library for all your reading. This layered storage system is called the '''Memory Hierarchy'''. The idea is to have some very fast but small memory close to the CPU, and then slower but much larger memory farther away.") Tag: Visual edit
16:1316:13, 9 July 2025 5.1.4 Instruction Cycle (Fetch, Decode, Execute, Store) (hist | edit) [2,486 bytes] Mr. Goldstein (talk | contribs) (Created page with "=== 5.1.4 Instruction Cycle (Fetch, Decode, Execute, Store) === Computers follow a set of steps over and over again to run programs. This cycle is called the '''Instruction Cycle''', and it has four main parts: # '''Fetch:''' The CPU goes to memory (like looking in a book) and gets the next instruction it needs to follow. # '''Decode:''' The CPU figures out what that instruction means. It's like translating a secret code into something it understands. # '''Execute:''' T...") Tag: Visual edit
16:1216:12, 9 July 2025 5.1.3 Registers (hist | edit) [1,802 bytes] Mr. Goldstein (talk | contribs) (Created page with "=== 5.1.3 Registers === '''Registers''' are tiny, super-fast storage areas right inside the CPU. Imagine them as small scratchpads where the CPU can quickly jot down important notes or numbers it's currently working on. Because they are so close to the CPU's brain, the CPU can access information in registers much faster than anywhere else.") Tag: Visual edit
16:0816:08, 9 July 2025 5.1.2 Control Unit (CU) (hist | edit) [1,798 bytes] Mr. Goldstein (talk | contribs) (Created page with "=== 5.1.2 Control Unit (CU) === The '''Control Unit (CU)''' is like the CPU's boss or traffic cop. It doesn't do the actual math, but it tells all the other parts of the CPU what to do and when to do it. It makes sure everything runs smoothly and in the correct order, like a conductor leading an orchestra.") Tag: Visual edit
16:0216:02, 9 July 2025 5.1.1 Arithmetic Logic Unit (ALU) (hist | edit) [1,679 bytes] Mr. Goldstein (talk | contribs) (Created page with "Think of the '''ALU''' as the "math and logic master" of the CPU. When your computer needs to add two numbers, subtract them, or even compare if one number is bigger than another, the ALU is the part that does it. It's like a super-fast calculator built right into the CPU!") Tag: Visual edit
16:0016:00, 9 July 2025 5.1 CPU Components (hist | edit) [4,093 bytes] Mr. Goldstein (talk | contribs) (Created page with "== 5.1 CPU Components == The '''CPU''' (short for Central Processing Unit) is like the brain of your computer. It's the part that does all the thinking and calculating. Just like your brain has different parts for different jobs (like remembering or solving problems), the CPU also has its own special parts.") Tag: Visual edit
15:5615:56, 9 July 2025 5.0 Computer Architecture & Organization (hist | edit) [3,086 bytes] Mr. Goldstein (talk | contribs) (Created page with "== 5.0 Computer Architecture & Organization == Imagine a computer as a super smart city. Just like a city has different parts that work together (like roads, buildings, and a mayor's office), a computer also has many parts that team up to get things done. '''Computer Architecture''' is like the blueprint of this city – it shows how all the pieces are designed and how they fit together. '''Computer Organization''' is like how the city actually operates and manages its t...") Tag: Visual edit
15:5215:52, 9 July 2025 4.1.2.4 Heaps (Min-Heap, Max-Heap) (hist | edit) [2,219 bytes] Mr. Goldstein (talk | contribs) (Created page with "=== 4.1.2.4 Heaps: Finding the Biggest (or Smallest) Fast === Imagine a special pile of toys where the biggest toy is always at the very top, easy to grab. Or, imagine a pile where the smallest toy is always at the top. A '''heap''' in computer science is like that special pile! It's a tree-like data structure (but usually stored in a simple array) that helps you quickly find and grab the largest or smallest item. Heaps have two main rules: # It's a "complete binary tr...") Tag: Visual edit
15:4915:49, 9 July 2025 4.1.2.3 Hash Tables (Hashing Functions, Collision Resolution) (hist | edit) [2,655 bytes] Mr. Goldstein (talk | contribs) (Created page with "=== 4.1.2.3 Hash Tables: Super-Fast Lookups === Imagine you have a huge closet with many numbered hooks, and you want to store your clothes so you can find them super fast. A '''hash table''' is like that closet! It's a data structure that helps computers store and find information incredibly quickly. Instead of looking through every item, a hash table uses a special trick: it takes the information you want to store (like a piece of clothing) and uses a special "hashing...") Tag: Visual edit
15:3515:35, 9 July 2025 4.1.2.2 Graphs (Directed, Undirected, Weighted) (hist | edit) [2,348 bytes] Mr. Goldstein (talk | contribs) (Created page with "=== 4.1.2.2 Graphs: Connecting Everything === Imagine a map of cities with roads connecting them, or a social media network where people are connected by friendships. A '''graph''' in computer science is a way to represent these kinds of connections! It's made up of: * '''Nodes (or Vertices):''' These are the "dots" or "points" in the graph, representing things like cities, people, or web pages. * '''Edges:''' These are the "lines" or "connections" between the nodes, re...") Tag: Visual edit
14:4514:45, 9 July 2025 4.1.2.1 Trees (Binary Trees, Binary Search Trees, AVL Trees, Red-Black Trees) (hist | edit) [3,890 bytes] Mr. Goldstein (talk | contribs) (Created page with "=== 4.1.2.1 Trees: Like a Family Tree for Data === Imagine a family tree, where one person is at the top (the oldest ancestor), and then they have children, who have children, and so on. A '''tree''' in computer science is very similar! It's a way to organize data in a hierarchy, starting from a single "root" node at the top. Each node can have "children" nodes below it, but each child only has one "parent." * '''Root:''' The very top node, like the first ancestor in a...") Tag: Visual edit
14:4214:42, 9 July 2025 4.1.2 Non-Linear Data Structures (hist | edit) [325 bytes] Mr. Goldstein (talk | contribs) (Created page with "== 4.1.2 Non-Linear Data Structures: More Complex Ways to Organize == While linear data structures arrange data in a line, '''non-linear data structures''' organize data in more complex ways, like branches, networks, or special groupings. This allows them to handle relationships between data that aren't just "next in line."") Tag: Visual edit
14:3714:37, 9 July 2025 4.1.1.4 Queues (FIFO, Priority Queues) (hist | edit) [2,192 bytes] Mr. Goldstein (talk | contribs) (Created page with "=== 4.1.1.4 Queues: First In, First Out (FIFO) === Now, imagine a line of people waiting for a roller coaster. The first person who gets in line is the first person to get on the ride. A '''queue''' in computer science is just like that! It follows a rule called '''FIFO''', which stands for '''First In, First Out'''. The first item you put ''in'' the queue is always the first item to come ''out''. * '''Adding items to a queue is called "enqueuing."''' (Like joining the...") Tag: Visual edit
14:3014:30, 9 July 2025 4.1.1.3 Stacks (LIFO) (hist | edit) [1,088 bytes] Mr. Goldstein (talk | contribs) (Created page with "=== 4.1.1.3 Stacks: Last In, First Out (LIFO) === Imagine a stack of plates. When you put a new plate on, it goes on top. When you take a plate off, you always take the one from the very top. A '''stack''' in computer science works exactly like this! It follows a rule called '''LIFO''', which stands for '''Last In, First Out'''. The last item you put ''in'' the stack is always the first item you can take ''out''. * '''Putting items on a stack is called "pushing."''' (L...") Tag: Visual edit
14:1214:12, 9 July 2025 4.1.1.2 Linked Lists (Singly, Doubly, Circular) (hist | edit) [2,983 bytes] Mr. Goldstein (talk | contribs) (Created page with "=== 4.1.1.2 Linked Lists: A Chain of Information === Imagine a treasure hunt where each clue tells you where to find the ''next'' clue. A '''linked list''' is similar! Instead of items being in numbered boxes like an array, each item (called a "node") holds its own piece of information ''and'' a pointer (or link) to the ''next'' item in the list. They're like a chain where each link knows where the next link is. This is different from arrays because items in a linked li...") Tag: Visual edit
13:5713:57, 9 July 2025 4.1.1 Linear Data Structures (hist | edit) [2,198 bytes] Mr. Goldstein (talk | contribs) (Created page with "= 4.1 Linear Data Structures: Organizing Information in a Line = Imagine you have a bunch of toys, and you want to keep them organized. You could line them up on a shelf, put them in a box one on top of the other, or make a train with them. In computer science, we have special ways to organize information (called "data") too! These ways are called '''data structures'''. Think of data structures as different kinds of containers or arrangements for your data. Just like...") Tag: Visual edit

22:2922:29, 8 July 2025 13.5 Ethical AI & Societal Impact (hist | edit) [3,092 bytes] Mr. Goldstein (talk | contribs) (Created page with "== 13.5 Ethical AI & Societal Impact == Imagine you have a super-smart robot that can do amazing things. That's a bit like Artificial Intelligence, or AI! But just like we need to make sure we use our own smarts for good, we also need to make sure AI is used in a fair and helpful way for everyone. This section is all about thinking about what's right and wrong when it comes to AI, and how AI can change our world. === 13.5.1 Bias in AI, Privacy Concerns === Sometimes, AI...") Tag: Visual edit
19:5519:55, 8 July 2025 10.4.2 Activation Functions (hist | edit) [1,100 bytes] Mr. Goldstein (talk | contribs) (Created page with "=== 10.4.2 Activation Functions === After a perceptron does its math, it needs to decide whether to "fire" or "activate" and send a signal to the next layer. This is where '''activation functions''' come in! Think of it like a light switch. If the total "strength" of the incoming signals is strong enough, the switch turns on, and the perceptron sends a signal forward. If it's not strong enough, the switch stays off. Activation functions are mathematical rules that help...") Tag: Visual edit
19:1619:16, 8 July 2025 10.4.1 Perceptrons, Layers (hist | edit) [1,881 bytes] Mr. Goldstein (talk | contribs) (Created page with "=== 10.4.1 Perceptrons, Layers === Let's break down our "fake brain": * '''Perceptron (The Basic Neuron)''': Think of a '''perceptron''' as a single, tiny decision-maker. It takes in some information (like numbers), does a little math with it, and then spits out a "yes" or "no" answer, or a number. It's like a tiny switch that turns on or off based on what it "sees." Imagine you're deciding if you should bring an umbrella to school. Your perceptron might take in inform...") Tag: Visual edit
19:0619:06, 8 July 2025 10.4 Neural Networks & Deep Learning (Basic Concepts) (hist | edit) [1,240 bytes] Mr. Goldstein (talk | contribs) (Created page with "== 10.4 Neural Networks & Deep Learning (Basic Concepts) == Imagine your brain! It's super good at learning new things, like riding a bike or recognizing your friend's face. It does this because it has billions of tiny cells called '''neurons''' that are all connected. When you learn, these connections get stronger or weaker, helping your brain make sense of the world. '''Neural Networks''' in computer science are like simplified versions of your brain. They are compute...") Tag: Visual edit
18:5518:55, 8 July 2025 10.3.4 K-Means Clustering (hist | edit) [4,957 bytes] Mr. Goldstein (talk | contribs) (Created page with "== 10.3.4 K-Means Clustering == Imagine you have a big pile of different toys, and you want to sort them into groups that are similar. You don't have any labels for the groups yet, but you want to find natural ways to put them together. K-Means clustering is like a smart helper that does this for you! It's a way for computers to sort a bunch of information (like your toys) into different groups, called '''clusters''', without being told what the groups should be. The go...") Tag: Visual edit
18:1418:14, 8 July 2025 10.3.3 Support Vector Machines (SVM) (hist | edit) [1,910 bytes] Mr. Goldstein (talk | contribs) (Created page with "=== 10.3.3 Support Vector Machines (SVM) === Imagine you have a bunch of red dots and blue dots scattered on a piece of paper, and you want to draw a straight line that best separates the red dots from the blue dots. '''Support Vector Machines (SVM)''' try to find the "best" line (or a more complex boundary in higher dimensions) that not only separates the groups but also maximizes the margin (the distance) between the line and the closest data points from each group. *...") Tag: Visual edit
17:5017:50, 8 July 2025 10.3.2 Decision Trees, Random Forests (hist | edit) [3,414 bytes] Mr. Goldstein (talk | contribs) (Created page with "=== 10.3.2 Decision Trees, Random Forests === These algorithms are powerful and intuitive, often used for both classification and regression tasks. They mimic human-like decision-making processes. ==== Decision Trees ==== Imagine you're trying to decide what to wear based on the weather. You might follow a mental flowchart: "Is it raining? If yes, wear a raincoat. If no, is it cold? If yes, wear a jacket. If no, wear a t-shirt." A '''Decision Tree''' works exactly like...") Tag: Visual edit
17:3317:33, 8 July 2025 10.3.1 Linear Regression, Logistic Regression (hist | edit) [3,295 bytes] Mr. Goldstein (talk | contribs) (Created page with "=== 10.3.1 Linear Regression, Logistic Regression === These two algorithms are fundamental in supervised learning and are often among the first ones learned when studying machine learning. They are used for prediction tasks. ==== Linear Regression ==== Imagine you have a scatter plot of data points showing how much ice cream is sold at different temperatures. As the temperature goes up, ice cream sales generally go up too. '''Linear Regression''' is like drawing the "be...") Tag: Visual edit
17:2217:22, 8 July 2025 10.3 Common ML Algorithms (hist | edit) [481 bytes] Mr. Goldstein (talk | contribs) (Created page with "=== 10.3 Common ML Algorithms === Now that we understand the different types of Machine Learning (Supervised, Unsupervised, Reinforcement), let's dive into some of the most common and fundamental algorithms used to make machines learn. These algorithms are the "recipes" that computers follow to find patterns, make predictions, or group data. We'll focus on algorithms that fall under Supervised and Unsupervised Learning, as they are widely used in many real-world applica...") Tag: Visual edit
16:3816:38, 8 July 2025 10.5.2.1 Generative Pre-trained Transformers (GPT) (hist | edit) [4,101 bytes] Mr. Goldstein (talk | contribs) (Created page with "==== 10.5.2.1 Generative Pre-trained Transformers (GPT) ==== The "GPT" in models like ChatGPT stands for '''Generative Pre-trained Transformer'''. This name precisely describes the core architecture and training methodology that makes these powerful language models work. Let's break down each part of the acronym: * '''Generative:''' ** '''What it means:''' This refers to the model's ability to '''create new content''' (specifically, text) that is original and coherent,...") Tag: Visual edit
16:0116:01, 8 July 2025 10.5.2 Large Language Models (LLMs) (hist | edit) [2,803 bytes] Mr. Goldstein (talk | contribs) (Created page with "=== 10.5.2 Large Language Models (LLMs) === Imagine a super-smart robot that has read almost every book, article, and piece of text ever written on the internet. It knows how words fit together, how sentences are formed, and even facts about the world. This robot can then use all that knowledge to create new text, answer questions, or even write stories! That's a bit like what a '''Large Language Model (LLM)''' is. LLMs are a special and very powerful type of Artificial...") Tag: Visual edit
15:4615:46, 8 July 2025 10.5.1 Basic tasks and applications (hist | edit) [3,541 bytes] Mr. Goldstein (talk | contribs) (Created page with "=== 10.5.1 Basic tasks and applications === Both NLP and Computer Vision are used in many ways every day. Here are some basic tasks and applications for each: '''Natural Language Processing (NLP) Tasks & Applications:''' # '''Text Translation:''' #* '''Task:''' Converting text from one human language to another. #* '''Application:''' Google Translate, which lets you translate websites, documents, or spoken words instantly. # '''Sentiment Analysis:''' #* '''Task:''' Fig...") Tag: Visual edit
15:3915:39, 8 July 2025 10.5 Natural Language Processing (NLP) / Computer Vision (CV) (hist | edit) [944 bytes] Mr. Goldstein (talk | contribs) (Created page with "=== 10.5 Natural Language Processing (NLP) / Computer Vision (CV) === Imagine a computer that can understand what you're saying, or "see" what's in a picture. That's what '''Natural Language Processing (NLP)''' and '''Computer Vision (CV)''' are all about! These are two huge and exciting areas within Artificial Intelligence where computers try to understand and work with human-like information. * '''Natural Language Processing (NLP):''' This is about teaching computers...") Tag: Visual edit
15:2715:27, 8 July 2025 10.2.3 Reinforcement Learning (hist | edit) [2,905 bytes] Mr. Goldstein (talk | contribs) (Created page with "==== 10.2.3 Reinforcement Learning ==== Imagine you're training a dog to do tricks. You don't tell the dog ''exactly'' how to sit; instead, you give it a treat (a reward) when it gets closer to sitting, and you don't give a treat (a punishment, or lack of reward) when it does something wrong. Over time, the dog figures out the right actions to get the treats. '''Reinforcement Learning (RL)''' is a type of Machine Learning where an "agent" (the computer program) learns t...") Tag: Visual edit
15:2215:22, 8 July 2025 10.2.2 Unsupervised Learning (Clustering, Dimensionality Reduction) (hist | edit) [2,847 bytes] Mr. Goldstein (talk | contribs) (Created page with "==== 10.2.2 Unsupervised Learning (Clustering, Dimensionality Reduction) ==== Imagine you have a big box full of mixed LEGO bricks of different colors and shapes, but no one tells you what to do with them. You start playing around and notice that certain colors tend to go together, or certain shapes fit nicely with others. You start sorting them into groups based on what you observe, without any instructions. '''Unsupervised Learning''' is a type of Machine Learning whe...") Tag: Visual edit
15:1215:12, 8 July 2025 10.2.1 Supervised Learning (Regression, Classification) (hist | edit) [2,510 bytes] Mr. Goldstein (talk | contribs) (Created page with "==== 10.2.1 Supervised Learning (Regression, Classification) ==== Imagine you're learning to identify different types of fruits. Someone shows you many pictures, and for each picture, they tell you: "This is an apple," "This is a banana," "This is an orange." After seeing many examples with the correct labels, you learn to identify new fruits on your own. '''Supervised Learning''' is the most common type of Machine Learning. It's like having a teacher (the "supervisor")...") Tag: Visual edit
14:5414:54, 8 July 2025 10.2 Types of Machine Learning (hist | edit) [604 bytes] Mr. Goldstein (talk | contribs) (Created page with "=== 10.2 Types of Machine Learning === Machine Learning (ML) is a huge and exciting part of AI. It's how computers learn to do things without being specifically told every single step. Instead, they learn from examples, just like you learn from experience. There are three main ways machines learn: # '''Supervised Learning:''' Learning from examples where we know the right answers. # '''Unsupervised Learning:''' Finding patterns in data without knowing the right answers...") Tag: Visual edit
14:3714:37, 8 July 2025 10.1.2 Applications of AI (hist | edit) [3,038 bytes] Mr. Goldstein (talk | contribs) (Created page with "==== 10.1.2 Applications of AI ==== AI is no longer just science fiction; it's everywhere around us! It's used in countless ways to make our lives easier, safer, and more fun. Here are just a few examples of how AI is being used today: # '''Voice Assistants:''' #* '''What it does:''' Understands your spoken words and responds to your commands. #* '''Examples:''' Siri, Google Assistant, Amazon Alexa. You can ask them questions, set alarms, play music, or control smart h...") Tag: Visual edit
14:2914:29, 8 July 2025 10.1 Introduction to AI (hist | edit) [2,703 bytes] Mr. Goldstein (talk | contribs) (Created page with "=== '''10.1 Introduction to AI''' === When we talk about Artificial Intelligence, it's helpful to understand that there are different levels of "smartness" we're trying to achieve in machines. ==== '''10.1.1 Strong AI vs. Weak AI''' ==== When scientists and engineers talk about AI, they often divide it into two main types: * Weak AI (or Narrow AI): ** What it is: This is the kind of AI we have today and use every day. Weak AI is designed and trained for a very specifi...") Tag: Visual edit
14:2114:21, 8 July 2025 10.0 Artificial Intelligence (AI) & Machine Learning (hist | edit) [1,239 bytes] Mr. Goldstein (talk | contribs) (Created page with "= 10.0 Artificial Intelligence (AI) & Machine Learning = Imagine a future where robots can talk to you like a human, cars can drive themselves, and computers can understand your voice commands. This amazing future is being built with '''Artificial Intelligence (AI)''' and '''Machine Learning (ML)'''! '''Artificial Intelligence (AI)''' is a big field in computer science that focuses on making machines "smart." It's about creating computer systems that can do things that...") Tag: Visual edit
13:3413:34, 8 July 2025 4.1 Data Structures (hist | edit) [792 bytes] Mr. Goldstein (talk | contribs) (Created page with "=== 4.1 Data Structures === As we just learned, a '''Data Structure''' is a specific way of organizing data in a computer's memory. It's not just about putting data in a list; it's about arranging it in a way that makes certain operations (like adding new data, finding data, or deleting data) very fast and efficient. Think of it as choosing the best container for your items: * If you have a stack of plates, you always add to the top and take from the top. * If you have...") Tag: Visual edit
13:3113:31, 8 July 2025 4.0 Data Structures Algorithms (DSA) (hist | edit) [1,241 bytes] Mr. Goldstein (talk | contribs) (Created page with "= 4.0 Data Structures & Algorithms (DSA) = Imagine you have a huge library with millions of books. If they were all just dumped in a giant pile, finding a specific book would be impossible! But if they're organized on shelves, by genre, by author, and with a catalog system, finding any book becomes much easier. In Computer Science, '''Data Structures''' are like the different ways you can organize and store information (data) in a computer's memory. They are special for...") Tag: Visual edit
13:1713:17, 8 July 2025 3.6.3 Debugging Techniques and Tools (hist | edit) [3,558 bytes] Mr. Goldstein (talk | contribs) (Created page with "=== 3.6.3 Debugging Techniques and Tools === Imagine you've built your LEGO castle, but one tower keeps falling over. You know there's a problem, but you don't know exactly ''why'' or ''where''! You need to become a detective. '''Debugging''' is the process of finding and fixing errors (bugs) in your computer program. It's a skill every programmer develops, and it often takes more time than writing the code itself! Here are some common techniques and tools used for deb...") Tag: Visual edit

9 July 2025

8 July 2025