Your learning guide to
CS6601 is a survey of the field of Artificial Intelligence and will often be taken as the first graduate course in the area. It is designed to be challenging and involves significant independent work, readings, and assignments. The course covers most of the required textbook Artificial Intelligence A Modern Approach 4th edition, which is a keystone of Georgia Tech Intelligent Systems PhD qualifier exam. More information can be found in the course website: https://omscs.gatech.edu/cs-6601-artificial-intelligence.
Topic list
- Minimax
(5/10)
- Alpha-beta pruning
(8/10)
- Performance improvement
(5/10)
- Sensitivity
(4/10)
- Optimization tricks
(2/10)
- Move-ordering
(6/10)
- Symmetry
(5/10)
- Iterative deepening
(3/10)
- Multiplayer games
(7/10)
- Probabilistic games
(8/10)
- Uninformed
(8/10)
- Breadth-first search
(6/10)
- Depth-first search
(2/10)
- Depth-limited search
(8/10)
- Informed
(3/10)
- Uniform-cost search
(2/10)
- Greedy search
(5/10)
- A* search
(9/10)
- Heuristics
(8/10)
- Bidirectional
(8/10)
- Tridirectional
(9/10)
- Tree vs. graph search
(4/10)
- Rationality
(9/10)
- PEAS
(8/10)
- Performance
(5/10)
- Environment
(8/10)
- Actuators
(2/10)
- Sensors
(6/10)
- Agent types
(4/10)
- Reflex
(4/10)
- Reflex with state
(5/10)
- Goal-based
(9/10)
- Utility-based
(6/10)
- Learning
(7/10)
- Hill-climbing
(9/10)
- Beam search
(3/10)
- Iterative improvement
(9/10)
- Simulated annealing
(6/10)
- Genetic algorithms
(3/10)
- Local vs. global maximum
(8/10)
- Local stochastic search
(8/10)
- Backtracking search
(3/10)
- Constraint propagation
(9/10)
- Arc consistency
(2/10)
- Advanced techniques
(9/10)
- Propositional logic
(8/10)
- First-order logic
(4/10)
- Resolution
(6/10)
- Unification
(5/10)
- Classical planning
(7/10)
- Planning graph
(8/10)
- Partial-order planning
(2/10)
- Ontological engineering
(9/10)
- Categories and objects
(4/10)
- Events
(5/10)
- Acting under uncertainty
(4/10)
- Independence
(2/10)
- Bayes rule and its use
(7/10)
- Naive Bayes models
(3/10)