Germany Jahrgangsstufe 13 Computer Science

Chapters: 4

1. Internet of Things

Internet of Things

Internet of Things (IoT) – The Internet of Things (IoT) is a network of everyday objects that have sensors, a small computer and a network connection, so they can collect data, share it over the internet and act on it. A sensor turns a physical change into data, a microcontroller processes it, the network (Wi-Fi, Bluetooth, Zigbee, 4G/5G, LoRa) carries it to a cloud server or app, and an actuator turns commands into action. IoT is described in three layers: perception, network and application. It brings convenience and saves energy, but needs strong privacy and security.

Artificial intelligence

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.

Formal languages and automata

Finite State Machines and Formal Languages – A finite state machine (FSM) has a fixed set of states, an alphabet of input symbols, a start state, a transition function that says which state comes next for each symbol, and (for an acceptor) a set of accepting states. It reads an input string one symbol at a time; if it ends in an accepting state the string is accepted. A Mealy machine also gives an output on each transition. The strings an FSM accepts form a regular language, which can also be described by a regular expression. Languages with nesting (like brackets) need more power: they are context-free and are written with BNF rules or syntax diagrams.

Algorithms, complexity and computability

Computational Complexity: Easy, Hard and Impossible Problems – Complexity measures how the number of steps grows as the input size n grows. Polynomial algorithms (n, n², n³) are tractable; exponential (2ⁿ) and factorial (n!) ones become hopeless very fast, so such problems are intractable. Some problems are easy to check but hard to solve (NP). Some, like the halting problem, cannot be solved by any algorithm at all.