DATA STRUCTURES AND ALGORITHMS 

  SE 2C03  (Winter 2018)

* * * 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: by email appointment

Course Assistance 

Eric Lefort (leforte@mcmaster.ca), office hours: Thur. 4:30-5:30 pm, Rm. ITB/115

Kelvin Lin (linkk4@mcmaster.ca), office hours: Mon. 4:30 - 5:30 pm, Mills Library 1st floor (send email)

Ahmed Khan (khana97@mcmaster.ca), office hours: Tue. 3:30 - 4:30 pm, Thode Library 1st floor (send email)

Schedule 

Term II, 2017/18
Tu Th Fr, 2:30 - 3:20 PM, Rm  TSH B105

Tutorials

Session 1:   Mo 2:30 - 3:20 PM, Rm T13 107
Session 2:   We 2:30 - 3:20 PM, Rm T13 107
Session 3:   Th 3:30 - 4:20PM, Rm BSB 117

Prerequisites 

SFWR ENG 2DM3

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:

• Worst case analysis of algorithms
• Basic searching algorithms (elementary sorts, quicksort, mergesort, heapsort)
• Basic sorting algorithms (binary search, search trees, hashing)
• Elementary data structures (stacks, queues, priority queues, search trees, heaps, hash tables, tries, graph representations)
• Graph algorithms (topological sort, breadth/depth-first-search, strongly connected components, minimum spanning trees, shortest paths)
• Basic string algorithms
• FSA's and Regular expressions

Students should be able to

• Analyze the running time of algorithms
• Identify the time/space trade-offs in designing data structures and algorithms
• Given a problem such as searching, sorting, graph and string problems, select from a range of possible algorithms, provide justification for that selection
• Understand implementation issues for the algorithms studied
• Reduce a given application to (or decompose it into) problems already studied

Learning objectives, indicators, and rubrics  

Outline of Topics (roughly per week) 

1. Introduction (1.3)
2. Analysis of algorithms - Union-Find  (1.4, 1.5)
3. Elementary sorts - Mergesort - Quicksort (2.1, 2.2, 2.3)
4. Priority queues - Sorting applications (2.4, 2.5)
5. Symbol tables - Binary search trees (3.1, 3.2)
6. Balanced search trees - Hash tables - searching applications (3.3, 3.4, 3.5)
7. Graph representation - BFS/DFS - Connected components - Topological sort (4.1, 4.2) 
8. Minimum Spanning Trees - Shortest paths (4.3, 4.4)
9. String sorts - Tries (5.1, 5.2)
10. Substring search - FSA's and regular expressions (5.3 except KMP, 5.4)

Student Assessment (Grading) 

• Homework assignments    15%
• Midterm exam                   40% (Feb. 15, 2:30 - 5:00 pm, Rm. HH 305 (last name A-K), UH 213 (last name L-Z), Open book (only original hardcopy of the textbook, no notes), Chapters 1, 2, 3.1-3.3)
• Final exam                         45% (Open book (only original hardcopy of the textbook, no notes), Chapters 1-5 (except 1.1,1.2, the KMP algorithm in 5.3, 5.5))
  

Policy on collaboration for homework assignments: Collaboration on the homework assignments is highly encouraged, within reasonable limits. Students are expected to discuss assignment problems with each other, and to cooperate on solutions in groups of no more than 5 people. However, the final write-up should be done by individual students (i.e. individual students should be able to explain their solutions by themselves, if such an explanation is asked for by the instructor) and the names of the collaborators should appear on the paper. Cooperation and teamwork are necessary for the success of any complex task (the design of algorithms being one such task), and it is the instructor's hope that students will come to appreciate them in a constructive way. Please see the instructor if you need someone to collaborate with.

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.

• "Introduction to Algorithms", 3rd ed., by T.H. Cormen, C.E. Leiserson, R.L. Rivest, and C. Stein. Note: This book is a great reference book, although somewhat more advanced.
• "Algorithms" by S. Dasgupta, C. Papadimitriou, and U. Vazirani. Note: This book is also an introductory text but in many topics it goes deeper, and also covers a somewhat different set of topics.
• "Introduction to the design and analysis of algorithms" by A. Levitin. Note: This text is also introductory, but its organization of the material is different to ours: it develops design techniques and then it applies them to problems, i.e., it's "technique oriented"; we take the "problem oriented" approach, where we study algorithms for specific problems and then we try to distil general techniques. The two approaches are not totally independent, but you may find it interesting to see another aspect of teaching the same material.
• "Algorithm design" by J. Kleinberg and E. Tardos. Note: A little more advanced text that is more oriented towards the design of algorithms and is as much concerned with the nitty-gritty of implementation.
• ...(your favourite book goes here)
• Me (yes, me, your instructor): If you find something interesting, or think of a problem you don't see how to solve, or want to know more about a topic, please make use of the office hours and come talk to me; I may be able to give you a pointer or two.  
  

Academic Dishonesty 

"Academic dishonesty consists of misrepresentation by deception or by other
fraudulent means and 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.

It is your responsibility to understand what constitutes academic
dishonesty.  For information on the various kinds of academic dishonesty
please refer to the Academic Integrity Policy, specifically Appendix 3,
located at http://www.mcmaster.ca/senate/academic/ac_integrity.htm

The following illustrates only three forms of academic dishonesty:
1.    Plagiarism, e.g. the submission of work that is not one's own or for which other credit has been obtained.  (e.g. submitting a copy of someone else's writeup for an assignment)
2.    Improper collaboration in group work. (e.g. collaboration between groups in an assignment)
3.    Copying or using unauthorized aids in tests and examinations."

Faculty Notices 

"The Faculty of Engineering is concerned with ensuring an environment that is free of all discrimination.  If there is a problem, individuals are reminded that they should contact the Department Chair, the Sexual Harrassment Officer or the Human Rights Consultant, as the problem occurs."
  
"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."

Assignments

All assignments are to be submitted to the instructor at the beginning of the lecture.

Assignment 1 (Due beginning of lecture Fri. 19/1):  The questions are here. The solutions are here.
Assignment 2 (Due beginning of lecture Fri. 2/2):  The questions are here. The solutions are here.
Assignment 3 (Due beginning of lecture Tue. 13/2):  The questions are here. The solutions are here.
Assignment 4 (Due beginning of lecture Thu. 22/3):  The questions are here. The solutions are here.
Assignment 5 (Due beginning of lecture Fri. 6/4):  The questions are here. The solutions are here.

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.