DATA STRUCTURES AND ALGORITHMS
SE 2C03 (Winter 2022)
* * * This outline
is constantly updated. Students should consult this page
regularly for all information relevant to this course * * *
Instructor
Dr. George Karakostas
ITB/218, ext 26132, Mac address: karakos
Office hours: Mon 5:00-6:00 pm
Course Assistance
Geneva Smith (smithgm@mac),
Office hour: Thu 10:30-11:30 am
John Popovici (popovj3@mac), Office hour: Mon 6-7 pm
Harshil Modi (modih1@mac), Office hour: Thu 4-5 pm
Luo Yi (luoy94@mac), Office
hour: Thu 5-6 pm
Scheduling notes
Course Objectives
In this course, we will study the concepts behind the technology of algorithms and data structures. The algorithmic solution of problems arises in any Computer Science or Software Engineering (indeed, any engineering or science) issue. Our goal will be: (i) To appreciate that algorithms and data structures are a technology (just as, say, electrical circuit design, or large software system design); this will be done by introducing performance measurement in order to measure algorithmic efficiency (in this course we will limit ourselves to efficient usage of time, and, sometimes, space). (ii) To recognize the details and nuances of modelling basic problems such as sorting, searching, graph exploration, etc. that will always show up in a Computer Scientist's or Software Engineer's career, and take advantage of these details in order to design better algorithmic solutions; not all algorithms that solve a problem are good, and no algorithm is good for all problems. (iii) To practice the art of reducing a given problem or application to other problems for which we already have good algorithms.
More specifically, students should know and understand:
Students should be able to
Learning objectives, indicators, and rubrics
Outline of Topics (roughly per week)
Student Assessment (Grading)
Policy on delayed assignments: Assignments delivered between the lecture they were due and the following lecture get 50% of total credit. After the following lecture, no credit given.
Policy on collaboration during exams: ABSOLUTELY NO COLLABORATION DURING EXAMS!!!
Resources
Required textbook:
"Algorithms", 4th ed., by R. Sedgewick and K. Wayne.
Recommended textbook: "Elements of programming interviews",
by A. Aziz, T-H. Lee, A. Prakash
Algorithms
visualization tools: There are many of those. One that
can be particularly useful is the tool developed by David
Galles and you can find here.
Other texts: There are many excellent (text)books that can be used for studying algorithms and data structures. Here I give only a couple you may find interesting, but don't feel discouraged from creating your own "algorithmic library" and finding the books that fit your needs. The subject of this course is rather challenging, and you will benefit if you hear more than one person(=book) explain a concept to you.
Assignments
All assignments are posted on Avenue, and
they are submitted electronically (also on Avenue)
To read the assignment files, you'll need the Adobe Reader, which is here.
Slides
The lecture slides
are a collection of textbook figures and other material. You
can find them at the textbook site here.
McMaster
Course Policies
The instructor and university reserve the right to
modify elements of the course during the term. The
university may change the dates and deadlines for any or all
courses in extreme circumstances. If either type of
modification becomes necessary, reasonable notice and
communication with the students will be given with
explanation and the opportunity to comment on changes. It is
the responsibility of the student to check their McMaster
email and course websites weekly during the term and to note
any changes.
ACADEMIC INTEGRITY
You are expected to exhibit honesty and use ethical behaviour in all aspects of the learning process. Academic credentials you earn are rooted in principles of honesty and academic integrity. It is your responsibility to understand what constitutes academic dishonesty.
Academic dishonesty is to knowingly act or fail to act in a way that results or could result in unearned academic credit or advantage. This behaviour can result in serious consequences, e.g. the grade of zero on an assignment, loss of credit with a notation on the transcript (notation reads: “Grade of F assigned for academic dishonesty”), and/or suspension or expulsion from the university. For information on the various types of academic dishonesty please refer to the Academic Integrity Policy, located at https://secretariat.mcmaster.ca/university-policies-procedures- guidelines/
The following illustrates only
three forms of academic dishonesty:
• plagiarism, e.g. the submission
of work that is not one’s own or for which other credit
has been obtained. • improper
collaboration in group work.
• copying or using unauthorized
aids in tests and examinations.
AUTHENTICITY / PLAGIARISM DETECTION
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ONLINE PROCTORING
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EXTREME CIRCUMSTANCES
The University reserves the right to change the dates and deadlines for any or all courses in extreme circumstances (e.g., severe weather, labour disruptions, etc.). Changes will be communicated through regular McMaster communication channels, such as McMaster Daily News, A2L and/or McMaster email.