China 高三 Information Technology
Chapters: 4
1. Sel.4 Introduction to AI
AI overview and history · Knowledge representation, heuristic search, Bayes, expert systems · Regression, decision trees, K-means · Neural networks and deep learning · Computer vision, NLP, reasoning, game playing · AI ethics and safety
- Artificial Intelligence: How Machines Learn to Think – Artificial intelligence (AI) is the skill of a computer system to do tasks that normally need human thinking: seeing, understanding speech, deciding and learning. An AI system is an agent that senses, thinks and acts. Old AI followed rules written by people. Modern AI mostly uses machine learning: it finds its own rule from many labelled examples (data). Neural networks are layers of simple units whose link strengths (weights) change during training. AI is used in maps, translation, health, farming and games. It can be wrong or unfair when its data is one-sided (bias), so people must check it, protect privacy and stay responsible.
- Knowledge Representation, Heuristic Search, Bayes and Expert Systems – Classic AI works in four ideas. Knowledge representation stores facts and rules so a computer can use them. Heuristic search uses a clue about the goal to check far fewer options than blind search. Bayesian reasoning updates a belief when new evidence arrives. An expert system joins a knowledge base of rules to an inference engine that answers like a human expert.
- Machine Learning – Machine learning (ML) is a way for computers to learn a rule from examples instead of being told the rule. We give data made of features (inputs) and, in supervised learning, labels (answers). The machine fits a model: regression predicts a number, a decision tree asks yes/no questions to pick a class, and k-means clustering groups unlabelled data. We train on most of the data, test on data it never saw, and measure accuracy or error. A model that only memorises (overfits) fails on new data.
- Neural Networks and Deep Learning – A neural network is a computer model made of many small units called artificial neurons. Each neuron multiplies its inputs by weights, adds them with a bias and passes the sum through an activation function. Neurons are arranged in layers: input, hidden and output. The network learns by training: it makes a guess, measures the error with a loss function, and changes the weights backwards (backpropagation with gradient descent) so the error becomes smaller. Networks with many hidden layers are called deep learning. CNNs are good at images, RNNs at sequences like text and speech.
- AI Skills: Computer Vision, Language, Reasoning and Game Playing – AI has four classic skills. Vision turns pixels into edges, shapes and labels. Language processing cuts text into tokens, tags words and finds meaning. Reasoning draws new facts from facts and rules, but only valid steps are safe. Game playing builds a tree of moves and scores it from the bottom up (minimax) to pick the best move.
- AI Ethics: Using Artificial Intelligence Fairly and Safely – AI systems learn patterns from data and then make decisions. AI ethics asks whether those decisions are fair, safe and respectful. The main issues are bias (unfair data gives unfair results), privacy (personal data needs consent and protection), transparency (people should know why an AI decided something), accountability (a human stays responsible), safety and misuse (deepfakes, false information), and social impact (jobs, the digital divide, the environment). Responsible AI means checking all of these before and after an AI is used.
2. Sel.5 3D design and creativity
3D design basics and technologies (modelling, rendering, VR, 3D printing) · Building and assembling 3D models · Designing creative 3D works
- 3D Modelling: From a Cube to a Printed Object – A 3D model is a shape stored in a computer as points (vertices) joined by edges into flat faces: a mesh. We build models by starting from simple shapes (primitives), moving, rotating and scaling them, and pulling faces out (extrude). Then we add materials and lights and the computer renders a picture. The same model can be sliced into thin layers and built by a 3D printer, or used in games, films, VR and product design.
- Building and Assembling 3D Models – A 3D model is built in three ways. Primitive modelling joins simple solids such as boxes, spheres, cylinders and cones. Profile modelling draws a flat shape and then extrudes (pulls) or revolves (spins) it. Assembly places finished parts in position, and an exploded view pulls them apart along straight lines to show how they fit.
- Designing Creative 3D Works: Plan, Design Rules, Animation and AR – A creative 3D work starts with a plan: who is it for and what should they feel? We build the model from simple shapes, then use design principles such as balance, proportion, contrast and a clear focus. For animation we set a few key poses (keyframes) and the computer fills the frames between them. For augmented reality (AR) the phone camera finds a marker or a flat surface and keeps the 3D model fixed on it.
3. Sel.6 Open-source hardware projects
Open-source hardware features · Input/output modules, communication, project workflow · Creative design and hardware choice · Making, testing, sharing
- Physical Computing: Making Code Sense and Move – Physical computing joins code to the real world. Sensors measure something (light, temperature, distance, button presses) and turn it into a number. A microcontroller runs a program that decides what to do with that number. Actuators (LEDs, buzzers, motors, screens) act on the world. This input–process–output cycle runs again and again in a loop. Using thresholds, conditions and feedback, we build night lights, smart plant waterers, wearables and interactive art.
- Open-Source Hardware Projects: Pins, Input/Output Modules, Communication and Workflow – An open-source hardware board is a small computer with pins. Digital pins read or write ON/OFF (HIGH or LOW). Analog pins read a changing level as a number, usually 0 to 1023. Power pins give 5 V, 3.3 V and ground. Input modules (button, sensor) send information in; output modules (LED, buzzer, motor) act on it. Boards talk to a computer or other boards by serial communication using TX and RX. A project runs setup once, then repeats a loop: read, decide, act and report.
- Creative Hardware Design: Needs Analysis, Function and Look, Choosing a Board – A good hardware project starts with a real need of a real user, not with a gadget. We write the need in one sentence, turn it into functions (what the device must do), design how it looks and feels, and then choose a board. A board is chosen by how many pins the modules need, whether Wi-Fi or Bluetooth is needed, the size, the power use and the price. Choose the smallest and cheapest board that fits, with a little room to grow.
- Making, Testing and Sharing a Hardware Project: Implementation, Testing, Sharing and IP – To make a hardware project, build one module at a time and check it before adding the next (implementation). Then test the whole project with test cases, including real conditions such as a dark room or very fast presses. When a test fails you have found a bug: find the cause, fix it and test again. Finally share your work: code, circuit diagram, parts list and a README in an online repository, with a licence that tells others what they may do. Intellectual property (IP) means the rights of the creator; open-source licences let others use and improve the work while giving credit.
4. Electives (选修)
Introduction to algorithms: greedy, divide and conquer, DP, backtracking; complexity · Mobile app design: sensors, storage, networking, security
- Algorithm Design Techniques – To design an algorithm, first specify the problem: the input data, the expected output and any conditions. Then write clear, finite steps in natural language, as a list, pseudocode or flowchart. Big problems are split top-down into smaller parts (stepwise refinement) or built bottom-up from small tested pieces. Classic techniques: brute force (try everything), divide and conquer (split, solve, combine; halving as in binary search and merge sort), greedy (take the best-looking choice each time; fast but not always optimal), dynamic programming (solve each small subproblem once and store it in a table) and backtracking (try a choice, undo it at a dead end). Choose the technique and data structures (arrays, stacks, binary trees) by checking correctness and efficiency (time complexity).
- Mobile App Design: Mobile Terminals, App Architecture, Sensors, Storage and Networking, Security – A phone is a small computer with a touch screen, sensors, storage and radios for networks. A mobile app is usually built in three layers: the screen (UI), the logic (rules) and the data. Apps read sensors such as the accelerometer, GPS and camera. They keep data in local storage on the phone and talk to servers over the internet using requests and responses. Good apps work offline and sync later. For security the app asks for permission before using sensors or files, and it locks (encrypts) private data and sends it only over secure connections.