- BFS
- DFS
- Evaluation of Search Algorithms
- DFS with a depth-limit L
- Iterative Deepening Search (IDS)
- IDS in Practice
- Uniform Cost Search
- Comparison of Uninformed Search Algorithms
- BFS, DFS, Uniform-Cost, Depth-Limited, Iterative Deepening
- Limitations of uninformed search
- Heuristic function
- Greedy best-first search
- A* Search
- Admissible heuristics
- Inventing heuristics via “relaxed problems”
- Techniques for generating heuristics
- Local search and optimization
- Hill-climbing search
- Hill climbing and local maxima
- Games–adversary
- Game tree
- Minimax strategy
- Two-Ply Game Tree
- Multiplayer games
- Alpha-Beta Pruning Algorithm
- Utility(or Evaluation) Functions
- Constraint Satisfaction Problems (CSP)
- Constraint graph
- Backtracking search for CSPs
- Improving backtracking efficiency
- Most constrained variable
- Least constraining value
- Forward checking
- Local search for CSPs
- Decision Tree Splits
- Good & Poor Splits
- Split Criteria
- Impurity (or Diversity) Measures
- Information Gain
- Gini Purity (Population Diversity)
- KNN
- Revisited
- Measuring K-Nearest Neighbors
- Scaling Attribute Value
- Distance-Weighted Nearest Neighbor Algorithm
- Voronoi Diagram
- Information retrieval (IR) System
- Document clustering
- Clustering Algorithms
- Flat algorithm
- Hierarchical algorithms
- K-Means (flat clustering algorithm)
- Termination conditions
- Convergence
- Time Complexity of K-Means Algorithm
- Initial Seed Choice in K-Means Algorithm
- Hierarchical Clustering Algorithm
- Hierarchical Agglomerative Clustering
- How to Combine Clusters?
- Single-link, complete-link, and group average-link clusterings
- Single Link Clustering
- Naïve Bayes & Bayesian Learning
- Bayesian Net & Inference
- ExpectationMaximization - Optional!
- Artificial Neural Networks I
- Artificial Neural Networks II