Programming & AI · Baccalaureate year 2, 2027 · Term 2
Chapter 7 explained: Machine Learning and Artificial Intelligence
The basics of machine learning, neural networks and deep learning, and large language models and generative AI.
Lessons in this chapter (3 lessons)
Lesson 7-1: The Basics of Machine Learning
Machine learning finds rules from data: supervised (labeled → classification with features), unsupervised (unlabeled → clustering), reinforcement (rewards). Mind its limits — bias and the black-box problem — and keep it supporting human judgment.
Lesson 7-2: Neural Networks and Deep Learning
A neural network = layers of neurons (input → hidden → output); each neuron weights and sums its inputs, and training tunes the weights on data. Deep learning stacks many hidden layers to build features (edges → parts → wholes), enabled by big data and GPUs.
Lesson 7-3: Large Language Models (LLM) and Generative AI
A language model generates text by predicting the next word (plausible, not always true → hallucination). LLMs use vast data and billions to trillions of parameters; RLHF tunes them by human feedback; multimodality unifies text, image, and audio. Fluency ≠ truth — always verify.
Train the Model
Chapter videos
The AI black box and the transparency problem
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