Asas AI

• Explain training and inference in plain language.
• Separate demonstrated capability from marketing claims.
• Evaluate AI outputs using evidence, uncertainty, and fit-for-purpose tests.

Prerequisites:None.

1. 1

   What AI is

   Keputusan: Distinguish AI, machine learning, and ordinary software.

   Latihan: Classify five everyday systems and explain which definition of AI you used.

   AI concepts and boundaries
   Apakah AI? →Asas Pembelajaran Mesin →
2. 2

   How systems learn

   Keputusan: Describe the role of examples, objectives, and generalization.

   Latihan: Diagnose why a model can perform well in testing but fail for a new population.

   Data, training, and generalization
   Bagaimana AI Belajar →Asas Pembelajaran Mesin →Latihan AI →
3. 3

   Models and inference

   Keputusan: Explain what happens when a trained model produces an output.

   Latihan: Trace one user request from input through model output and human review.

   Model, inferens, dan had
   Model AI Diterangkan →Inferens AI →Rangkaian Neural →
4. 4

   Evidence and uncertainty

   Keputusan: Check claims, benchmarks, and confident-sounding outputs.

   Latihan: Create a claim ledger for one AI product announcement.

   Penilaian dan penanda aras literasiUncertainty and confidence calibrationPengesahan sumber dan tuntutan
   Penanda Aras AI →Halusinasi AI →Mitos AI →