Computer Science 252
Algorithms and Data Structures
Last update: September 17, 2022

Winter 2023 --- Course Syllabus





New / Messages  


September 17, 2022: I condemn in the strongest possible terms McGill University's decision not to require vaccinations for in-person classes.


Instructor  


Luc Devroye | Email to lucdevroye@gmail.com | McConnell Engineering Building, Room 300N | Office hours: TBA.


Time and location


Tuesday & Thursday, 2:30-4pm, McConnell Engineering 13.
January 5: First lecture
April 13: Last lecture.


The supplemental


If you write a supplemental or deferred final exam, then that exam will count for 100% towards your grade---midterms and assignments will be irrelevant.

Teaching assistants: Office hours, tutorials

TBA

The TA hours TBA


Lectures (2022)


Material covered in each lecture in 2022.


Objectives


  • Introduce the student to algorithmic analysis.
  • Introduce the student to the fundamental data structures.
  • Introduce the student to problem solving paradigms.


Contents


Part 1. Data types.

  • Abstract data types.
  • Lists. Linked lists. Examples such as sparse arrays.
  • Stacks. Examples of the use of stacks in recursion and problem solving.
  • Queues.
  • Trees. Traversal. Implementations. Binary trees.
  • Indexing methods. Hashing.
  • Introduction to abstract data types such as mathematical set, priority queue, merge-find set and dictionary.
  • Heaps.
  • Binary search trees, balanced search trees.
  • Tries, suffix trees.
  • Data structures for coding and compression.

Part 2. Algorithm design and analysis.

  • The running time of a program.
  • Worst-case and expected time complexity.
  • Analysis of simple recursive and nonrecursive algorithms.
  • Searching, merging and sorting.
  • Amortized analysis.
  • Lower bounds.
  • Introductory notions of algorithm design:
    • Divide-and-conquer. Recurrences. The master theorem. Quicksort. Other examples such as fast multiplication of polynomials and matrices. Fast Fourier transform.
    • Dynamic programming. Examples such as Bellman-Ford network flow, sequence alignment, knapsack problems and Viterbi's algorithm.
    • Greedy methods. This includes the minimal spanning tree algorithm and Huffman coding.
  • Graph algorithms.
    • Depth-first search and breadth-first search
    • Shortest path problems
    • Minimum spanning trees
    • Directed acyclic graphs
    • Network flows and bipartite matching


Evaluation


Assignments 42%, midterm 8%, final 50%.


Prerequisites


Computer Science 250. Mathematics 240. Recommended background: Mathematics, discrete mathematics, arguments by induction. Restricted to Honours students in Mathematics and/or Computer Science.


Textbook


T.H. Cormen, C.E.Leiserson, R.L.Rivest, and C. Stein: Introduction to Algorithms (Third Edition), MIT Press, Cambridge, MA, 2009. Amazon link. Excellent pirated copies of this book are navigating the web. A free PDF file is available from McGill's Library. Github offers pages with solutions of all exercises.

Another appropriate text, with a different focus (more algorithms, fewer data structures) is by J. Kleinberg and E. Tardos: "Algorithm Design". Pearson, Boston, 2006.

Finally, scribes in 2017, 2018, 2019, 2020 and 2022 made notes on the following topics:


Information for the scribes


We will use LaTeX to first create a TeX file (for the body of the text) and a bib file (for bibliography), and then create a PDF file from this. The prototypes below are courtesy of Ralph Sarkis.