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COMP 5413

Advanced Topics in Computer Science

0.5 credits · Graduate

Winter 2024

Topics

  • 2: Introduction to Algorithms
  • Overview of the course, objectives, and expectations
  • Basics of algorithm design and analysis
  • Introduction to Cormen et al.'s "Introduction to Algorithms
  • Computational Logics and Reasoning
  • 4: Foundations in Algorithms Analysis and Computational Complexity
  • Big-O notation and algorithm complexity
  • Lower and upper bounds for time and space complexity
  • Analyzing and comparing algorithms
  • Assignments on algorithm analysis
  • 6: Graph Algorithms
  • Graph theory fundamentals
  • Greedy Algorithms
  • Dynamic Programming
  • Practical applications and case studies
  • 8: Linear Programming and Integer Programming
  • Introduction to linear programming
  • Integer programming and Branch and Bound method
  • Applications in optimization problems
  • Problem-solving sessions
  • 10: Searching and Backtracking
  • Search algorithms and strategies
  • Backtracking techniques
  • Solving complex problems through search and backtracking
  • Implementing search algorithms

Readings

  • Required: Cormen, Leiserson, Rivest, and Stein, Introduction to Algorithms, 4th Edition, MIT Press, 2022. Brachman and Levesque. Knowledge Representation and Reasoning, Morgan Kaufmann, 2004. Russell and Norvig. Artificial Intelligence: A Modern Approach, 4th Edition, Pearson, 2021.
Sources