Lectures

Lectures notes will appear here.

• Lecture 1: Graphs
  Basic concept of graphs, Graph representation
• Lecture 2: Graph Search
  Breadth First Search, Depth First Search
• Lecture 3: Minimum Spanning Trees
  Prim's Algorithm, Kruskal's Algorithm
• Lecture 4: Shortest Paths
  Dijkstra's Algorithm
• Lecture 5: Network Flows
  Maxflow, mincut, Ford-Fulkerson Algorithm
• Lecture 6: Reductions
  Reductions, Lower bounds, Classifying problems
• Lecture 7: Linear Programming
  Simplex Algorithm, reductions
• Lecture 8: Intractability
  Intractability, search problems, P vs NP, NP-completeness, dealing with intractability
• Lecture 9: Introduction to Artificial Intelligence
  What is AI?
• Lecture 10: Problem Solving & Search 1
  Search problems, state space, uniformed search
• Lecture 11: Problem Solving & Search 2
  Informed search, A*
• Lecture 12: Local Search
  Local search problems, hill-climbing, simulated annealing, genetic algorithms
• Lecture 13: Propositional Calculus 1
  Propositions and models, propositional syntax & semantics, entailment, inference
• Lecture 14: Propositional Calculus 2
  Proof systems, resolution, proof by contradiction
• Lecture 15: Predicate Calculus
  Limitations of propositional calculus, resolution
• Lecture 16: Expert Systems
  Knowledge representation, reasoning, inference
• Lecture 17: Reasoning about Actions
  Situation calculus, Blocks world, frame problem
• Lecture 18: Reasoning about Actions 2
  STRIPS planning, regression planning
• Lecture 19: Machine Learning
  Kinds of learning
• Lecture 20: Decision Tree Learning
  Classification and regression
• Lecture 21: Neural Networks
  Units, links and activation functions, network structures