ECE250 Data Structures and Algorithms (Winter 2024)

Overview

The objective of this course is to introduce students to data structures (linked lists, binary search trees, hash tables, graphs), Abstract Data Types (Stacks, Queues, Maps, Sets), algorithms (sorting, graph search, minimal spanning tree) and algorithms design techniques(greedy, divide and conquer, dynamic programming, etc) Prereq: ECE 150 (or equivalent)

Ahmad Nabil Yousef Kamal (anykamal@uwaterloo.ca)

Soroush Mortazavimoghaddam (soroush.mortazavimoghaddam@uwaterloo.ca)

Sheikh Abrar Tahmid (sheikh.abrar.tahmid@uwaterloo.ca)

Prajwal Thakur (prajwal.thakur@uwaterloo.ca)

Lectures, tutorials, labs

For lecture, tutorial and lab schedules, see the Undergraduate Schedule of Classes.

Makeup Lectures

We have 5 makeup lectures tetatively scheduled throughout the term. Whether they will be used will depend on the pace of the course. Students will be notified (in class & through emails) beforehand if we intend to use any of them.

Tutorials

We will walk through example problems that help students strengthen their understanding of lecture materials and prepare for exams. (Tutorials will start from the 2^nd week)

Discussion Forum

We will use Piazza for class discussions. The system is highly catered to getting you help fast and efficiently from classmates, the TAs, the lab instructor and myself. Rather than emailing questions to the teaching staff, you should post your questions on Piazza. The Piazza course site is here

Office Hours

Patrick: by appointment

TAs: druing lab sessions & by appointment (Please email all the TAs when booking an appointment)

Textbook

We will be mostly using lectures notes posted to Learn, however students are encouraged to use the following optional texts to supplement their learning:

Mark Allen Weiss, Data Structures and Algorithm Analysis in C++, 4th Ed., Addison Wesley, 2012.

Cormen, Leiserson, Rivest, and Stein (CLRS), Introduction to Algorithms, 2nd Ed., MIT Press, 2001.

Grading

Lab Projects: 30%

Midterm Exam: 30%

Final Exam: 40%

Labs Projects

There will be 5 lab projects in the course. Each lab project will be given about 2 weeksto finish. Lab projects are done individually. Details about the labs will be posted on Learn.

	Release date	Due date	Weight
Lab0	Jan 16	Jan 29	10%
Lab1	Jan 30	Feb 12	15%
Lab2	Feb 13	Mar 11	25%
Lab3	Mar 12	Mar 25	20%
Lab4	Mar 22	April 8	30%

Exams

Midterm exam is currently scheduled on Feb 27 at 8:30am. Final exam schedule will be posted once available. Both will be closed-book exams.

Late Policy

Late submission incur a 25% penalty when < 24 hours late, a 50% penalty when > 24 but < 48 hours late, and receive no credit when > 48 hours late. No late submission of exams are accepted.

Academic Policy

The discussion of ideas and problem-solving strategies is an integral part of the learning experience, but cheating and plagiarism are not. Practically, you violate academic integrity when (1) you obtain solutions and code from others, or (2) you provide solutions and code to others. A student is expected to know what constitutes academic integrity to avoid committing an academic offence and to take responsibility for his/her actions. A student who is unsure whether an action constitutes an offence, or who needs help in learning how to avoid offences (e.g., plagiarism, cheating) or about “rules” for group work/collaboration should seek guidance from the course instructor, academic advisor, or the undergraduate Associate Dean. For information on categories of offences and types of penalties, students should refer to Policy 71, Student Discipline. For typical penalties check Guidelines for the Assessment of Penalties.

Schedules(tentative) & Topics

Week of	Topics
Jan 8	Introduction and Logistics
Jan 15	Algorithm Analysis & Abstract Data Types
Jan 22	Lists, Stacks, Queues Lab 0
Jan 29	Trees, Binary (Search) Trees, Tree Traversals
Feb 5	AVL Trees, Red-Black Trees Lab 1
Feb 12	Heaps, Priority Queues
Feb 19	Reading Week
Feb 26	Midterm Week
Mar 4	Hashing, Hash tables Lab 2
Mar 11	Sorting Algorithms
Mar 18	Introduction to Graphs, Graph Traversals Lab 3
Mar 25	Graph Algorithms
April 1	Algorithm Design Techniques Lab 4

Ziqiang Patrick Huang

Asistant Professor, Teaching Stream of Electrical and Computer Engineering

PhD ECE, Duke University