• L01: Introduction
  Additional Reading: Notes
  tl;dr: introduction lecture
• L02: State Space Search: Modeling and Complexity
  tl;dr: depth-first search, breadth-first search, complexity analysis, iterative deepening
• L03: Roadmap Planning and Informed Search
  Additional Reading: Notes
  tl;dr: informed search, best-first search, dynamic programming
• L04: Activity Planning as Heuristic Forward Search
  tl;dr: activity planning, classical planning, planning graph, heuristic forward search
• L05: Games and Alpha Beta Search
  tl;dr: game trees, optimal strategies, pruning, alpha go, stochastic games
• L06: Markov Decision Processes
  Additional Reading: Notes
  tl;dr: mdp, policies, value functions
• L07: Probabilistic Planning
  tl;dr: ao*, lao*
• L08: Hidden Markov Models
  tl;dr: markov chains, hmms, filtering, prediction, smoothing
• L09: Propositional Logic
  Additional Reading: Notes
  tl;dr: model-based reasoning, logic, propositional logic, proofs
• L10: Propositional Satisfiability
  Additional Reading: Notes
  tl;dr: systematic search, deduction, unit propogation, directed resolution
• L11-A: Sampling-based Path Planning
  tl;dr: configuration space, graph search, prm, rrt
• L11-B: Monte-Carlo Tree Search
  tl;dr: monte-carlo tree search, upper-confidence tree search
• L12: Self-Repairing Systems
  Additional Reading: Notes
  tl;dr: state-based programs, consistency-based diagnosis, (probabilistic) mode estimation
• L13: Conflict-Directed Search
  tl;dr: diagnosis using conflicts, optimal satisfiability using conflicts
• L14: Constraint Programs: Propgation
  tl;dr: arc consistency, constraint propagation, numeric constraints
• L15: Constraint Programs: Vehicle Routing, Time and Resources
  Additional Reading: Notes
  tl;dr: multi-vehicle routing, global constraints
• L16: Solving Constraint Programs: Search, Forward Checking and Elimination
  Additional Reading: Notes
  tl;dr: systematic search, propagation, stochastic local search, elimination for constraints
• L17: Guest Lecture: Autonomous Ocean Exploration
  tl;dr: guest lecture
• L18: Bayesian Inference: Exact Inference
  Additional Reading: Notes
  tl;dr: bayes nets, exact inference
• L19: Bayesian Inference: Approximate Inference
  tl;dr: sample generation, markov chain methods, particle filtering
• L20: Scheduling of Temporal Plans
  Additional Reading: Notes
  tl;dr: temporal constraints, constraint inference, consistency, scheduling, robust execution
• L21: Mathematical Programming: Modeling, Elimination and Intuitions
  tl;dr: linear programs, gaussian elimination
• L22: Linear Programming and Simplex
  Additional Reading: Notes
  tl;dr: linear programs, gaussian elimination, simplex
• L23: Integer Programming and Branch and Bound
  Additional Reading: Notes
  tl;dr: binary integer programs, integer programs, disjunction programs, branch and bound
• L24: Convex Optimization
  tl;dr: convexity, local and global optima, nonlinear constraints
• L25: Risk-Bounded Planning
  tl;dr: risk-bounded motion planning, risk-bounded task planning
• L26: Course Review
  tl;dr: final review of the course