Methods of AI MOC

🗺️ Methods of AI — Master Index (MOC)

The one note to start from. Every topic → which file holds the deep content, its Lernzettel, its quiz, and the loose atomic notes. Plus an algorithm locator at the bottom: “where do I find AC-3 / A* / Bellman?” answered once and for all.

⚠️ Naming gotcha: the comprehensive Local-Search note is called Search Algorithms, not “Local Search”. And AC-3 lives in the CSP note, not in Search Algorithms — it’s a constraint algorithm, not a search algorithm.

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Topic map (Session → files)

#	Topic	📘 Deep note	📋 Lernzettel	✏️ Quiz	🧩 Atomic notes
02	Local Search	Search Algorithms ⭐	lernzettel_local-search_30-04-26	quiz_local-search_30-04-26	Local Beam Search, Stochastic Diffusion Search
03	Constraint Satisfaction	Constraint Satisfaction Problems ⭐	lernzettel_csp_30-04-26	quiz_csp_30-04-26	—
04	Planning I (Classical)	planning-i_30-04-26	lernzettel_planning-i_30-04-26	quiz_planning_30-04-26	STRIPS, Why an an AI struggles with Planning
05	Planning II (MDP)	planning-ii_30-04-26	lernzettel_planning-ii_30-04-26	quiz_planning_30-04-26	—
06–07	Knowledge Representation	knowledge-representation_30-04-26	lernzettel_knowledge-representation_30-04-26	quiz_knowledge-representation_30-04-26	—
08	Vagueness & Uncertainty	vagueness-uncertainty_30-04-26	lernzettel_vagueness-uncertainty_30-04-26	quiz_vagueness-uncertainty_30-04-26	—
09–10	Machine Learning I & II	ml-i-ii_30-04-26	lernzettel_ml-i-ii_30-04-26	quiz_ml_30-04-26, quiz_decision-trees_18-05-26	Machine Learning I & II, Machine Learning
11	Machine Learning III (SVM)	svm_30-04-26	lernzettel_svm_30-04-26	quiz_svm_30-04-26	Support Vector Machines
12–13	Neural Networks & Deep Learning	neural-networks-deep-learning_30-04-26	lernzettel_neural-networks-deep-learning_30-04-26	quiz_neural-networks_30-04-26	Neural Networks & Deep Learning, Transformers, Deep Neural Networks

Cross-topic resources:

• 🧪 quiz_mega-all-topics_23-05-26 — 100 Qs across all 9 topics (exam dress rehearsal)
• 📄 Methods of AI — Exam Cheat Sheet — 1-page compression of all topics
• ❓ Questions for Methods of AI — 100 worked Q&A
• 📚 Methods of AI Lecture — original lecture index / superlink hub

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🔎 Algorithm locator — “where do I find …?”

Algorithm / concept	Lives in	Lecture?
BFS, DFS, Uniform Cost, A*	Uninformed Search (dedicated note, with definitions + comparison) — also referenced from Search Algorithms	❌ prerequisite, not a MoAI session
Hill Climbing (all variants), Simulated Annealing, Beam Search, Genetic Algorithm	Search Algorithms	✅ Session 02
Node Consistency, AC-1, AC-3, PC-2, k-consistency	Constraint Satisfaction Problems	✅ Session 03
Forward Checking, MAC, Backtracking, MRV / LCV / Degree, Min-Conflicts	Constraint Satisfaction Problems	✅ Session 03
RCC-8, Allen’s relations	Constraint Satisfaction Problems + knowledge-representation_30-04-26	⚠️ KR, not Session 03
STRIPS, PDDL, frame/qualification/ramification problem	planning-i_30-04-26, STRIPS	✅ Session 04
MDP, Bellman equation, Value / Policy Iteration	planning-ii_30-04-26	✅ Session 05
TBox/ABox, ALC, OWL, Tableaux	knowledge-representation_30-04-26	✅ Session 06–07
Fuzzy logic, t-/s-norms, Dempster-Shafer	vagueness-uncertainty_30-04-26	✅ Session 08
Decision Trees, Random Forest, bagging, Information Gain	ml-i-ii_30-04-26, quiz_decision-trees_18-05-26	✅ Session 09–10
SVM, kernel trick, KKT, Perceptron	svm_30-04-26, Support Vector Machines	✅ Session 11
Hopfield, MLP, ReLU, backprop, Autoencoder	neural-networks-deep-learning_30-04-26	✅ Session 12
Transformers, self-attention, BERT/GPT	neural-networks-deep-learning_30-04-26, Transformers	✅ Session 13

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See also

Tags: methods-of-ai moc ai-generated
Superlink: Methods of AI Lecture