Learning record
Self-declaration — not verified
has worked through the learning path “AI literacy basics” by KI einfach verstehen: read every lesson in full and answered the questions of their recall moments for self-assessment.
- Issued on
- Scope
- 6 lessons, estimated reading time about 1.5 hours (84 minutes, estimated from the text length), plus 38 questions in recall moments
Learning goals
After the learning path, the learner should be able to:
- Tell program, algorithm and AI model apart, and explain why a model is not made of written-down rules but of numbers that are set during training.
- Describe what an AI model receives as input, why its output is usually a list of ratings, and how an answer is built from it piece by piece.
- Explain how a tokenizer splits text into reusable pieces, numbers them through a fixed vocabulary, and so produces the numerical input of a language model.
- Place scalar, vector, matrix and tensor, and explain why a chat message or a photo is a block of numbers to a model.
- Explain how a language model turns scores into probabilities, why it does not always pick the obvious option, and what temperature changes about that.
- Describe what is inside a finished model, what is fixed before training, why training costs more than inference, and what hardware a model needs.
Lessons worked through
- Program, Algorithm, Model Comparedhttps://ki-einfach-verstehen.de/en/lessons/program-algorithm-model/
- Input and Output: What a Function Doeshttps://ki-einfach-verstehen.de/en/lessons/input-and-output/
- Tokenizers: How Language Becomes Numbershttps://ki-einfach-verstehen.de/en/lessons/tokenizer-ids-vocabulary/
- Scalar, Vector, Matrix, Tensor: the Building Blocks of Numbershttps://ki-einfach-verstehen.de/en/lessons/scalar-vector-matrix-tensor/
- Probability and Softmax: How a Model Decideshttps://ki-einfach-verstehen.de/en/lessons/probability-and-softmax/
- Parameters, Training vs. Inference, Hardware: How a Model Runshttps://ki-einfach-verstehen.de/en/lessons/parameters-training-inference-hardware/
Not covered by this learning path
legal questions such as obligations under the EU AI Act, data protection, or copyright · risks and limits of specific AI systems in your own work context · how to operate particular tools or write prompts
This learning record was created by the learner themselves. KI einfach verstehen has verified neither their identity nor their learning outcome. The record deliberately carries no score and no grade. It can be part of the documentation of AI literacy measures (Art. 4 EU AI Act).