Artificial Intelligence (classical AI and soft computing)
The planned outline of 16 modules. Lessons are written, run and reviewed before they are published.
Before this track
Planned outline
- Module 1
Start here: what AI is and how this track works
Coming soon - Module 2
Problem solving as search
Coming soon - Module 3
Heuristic search
Coming soon - Module 4
Local search and optimisation
Coming soon - Module 5
Genetic algorithms
Coming soon - Module 6
Adversarial search and games
Coming soon - Module 7
Constraint satisfaction problems
Coming soon - Module 8
Propositional logic and SAT
Coming soon - Module 9
First-order logic and inference
Coming soon - Module 10
Knowledge representation and expert systems
Coming soon - Module 11
Automated planning
Coming soon - Module 12
Uncertainty and Bayesian networks
Coming soon - Module 13
Reasoning over time
Coming soon - Module 14
Decisions under uncertainty
Coming soon - Module 15
Soft computing: fuzzy logic and classic neural models
Coming soon - Module 16
Learning agents and classical AI today
Coming soon
Related tools
Official documentation
- aima.cs.berkeley.edu/contents.html (aima.cs.berkeley.edu)
- pyodide.org/en/stable/usage/loading-packages.html (pyodide.org)
- pysathq.github.io/docs/html/api/solvers.html (pysathq.github.io)
- doi.org/10.2200/S00900ED2V01Y201902AIM042 (doi.org)
- gate2027.iitm.ac.in/static/doc/GATE2027_Syllabus/DA_GATE2027_Syllabus.pdf (gate2027.iitm.ac.in)
More in AI and machine learning
- Machine Learning (scikit-learn) (coming soon)
- AI Literacy & Prompting (coming soon)
- Building LLM Applications (coming soon)
- Mathematics for Machine Learning (coming soon)
- All AI & ML tracks
- How we make lessons