Chapter 7 · Watch, then practise
Application of AI
Connect reasoning and learning methods to concrete applications while stating what each system consumes and produces.
3 questions · 3 with related videos. Matches are based on playlist titles; broader background matches are labeled.
What to study
- Rule-based expert systems
- Natural language tasks
- Neural-network applications
- Task-specific evaluation
Chapter playlists
Choose a playlist
Notes
Expert System Architecture In Artificial Intelligence Explained (HINDI)
5 Minutes Engineering · 4:30
Expert-system architecture matches separating a rule base, inference mechanism and explanation interface.
1. Expert-system structure
How could a rule-based system assist equipment fault diagnosis?
Store observations as facts and encode domain rules that infer possible faults or the next check. An inference engine applies those rules; an explanation can identify which facts triggered them. Such a system reflects the knowledge encoded by its designers and needs maintenance when equipment or procedures change.
Introduction To Natural Language Processing In Artificial Intelligence (HINDI)
5 Minutes Engineering · 3:31
Choose a video · 2 lectures
NLP introduction and components support distinguishing language tasks; use the answer for the specific three-way comparison.
2. Language tasks
How are speech recognition, translation and information extraction different?
Speech recognition maps audio to words; translation maps content between languages; information extraction identifies structured items such as entities or relationships in text. They can be combined in an application, but their inputs, outputs and evaluation targets differ.
Machine Learning Fundamentals: The Confusion Matrix
StatQuest with Josh Starmer · 7:13
Choose a video · 2 lectures
The supplementary confusion-matrix and sensitivity/specificity lessons support evaluating errors beyond overall accuracy.
3. Select an evaluation
For a classifier flagging faulty parts, why is overall accuracy insufficient?
A model can look accurate by predicting the common class while missing rare faults. Examine which cases are falsely accepted or rejected, evaluate representative held-out data and choose an error tradeoff suited to the application. Training loss alone does not establish deployment performance.