


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 inperson classes.

Instructor

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

Time and location

Tuesday & Thursday, 2:304pm, 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 grademidterms
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, mergefind 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.
 Worstcase and expected time complexity.
 Analysis of simple recursive and nonrecursive algorithms.
 Searching, merging and sorting.
 Amortized analysis.
 Lower bounds.
 Introductory notions of algorithm design:
 Divideandconquer. Recurrences. The master theorem. Quicksort. Other examples such as fast multiplication of polynomials and matrices. Fast Fourier transform.
 Dynamic programming. Examples such as BellmanFord network flow, sequence alignment, knapsack problems and Viterbi's algorithm.
 Greedy methods. This includes the minimal spanning tree algorithm and Huffman coding.
 Graph algorithms.
 Depthfirst search and breadthfirst 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.


