Algorithms for Modern Data Sets (COMP 3801)   

Weekly Schedule


Instructor: Anil Maheshwari
Office: HP 5125b

Lectures: Lectures are Mondays and Wednesdays at 14:35 to 15:55 PM. See public class schedule for the location.

Office hours Mondays 8:35-09:45 AM (HP 5125b)

Please feel free to send me email at

Teaching Assistant:  Hao Yan

Tuesdays 10 - 11 AM and Thursdays 3 - 4 PM in HP 4125 starting on September 14th.

Course objectives:  Algorithmic design techniques for modern data sets arising in, for example, data mining, web analytics, epidemic spreads, search engines and social networks. Topics may include data mining, hashing, streaming, clustering, recommendation systems, link analysis, dimensionality reduction, online, social networking, game theoretic and probabilistic algorithms.

Caution: Note that you need a minimum of B+ in COMP 2804 to register in this course. The contents of this course are fairly broad, and will cover a spectrum of techniques from the design and analysis of algorithms. It is assumed that you have a very good grasp on the analysis of algorithms (O-notation, recurrences, and complexity analysis), elementary probability theory including expectation and indicator random variables (contents of COMP 2804), the knowledge of basic data structures (lists, trees, hashing), and the knowledge of discrete mathematics (counting, permutations and combinations,  proof techniques:  induction, contradiction, ..). Note that there will not be time to review these material, and to appreciate the contents of this course, you must have a very good grasp on these topics. 


Useful References related to various topics:


We will likely cover parts of MMDS Chapters 3, 4, 5, 7, 8, 9, 11, and possibly 10 and 6.  In addition to this there will be more material on Data Streaming from some research articles. In broad terms, some combination of the following topics:

Link Analysis
Mining Data Streams - Frequency and Moment Estimates
Finding Similar Items - Locality Sensitive Hashing
Advertising on the web - Adwords & Online matching
Recommendation Systems - Collaborative Filtering
Dimensionality Reduction - Eigenvalues, PCA, SVD
Clustering - K-Means
Mining Social Networks - Community Detection, Partitioning of Graphs,  Dynamic Graph Algorithms
Frequent Itemset
+ some probability+linear algebra as and when required.

Grading Scheme (Tentative):

- Assignments: 3x12%=36%

Please only refer to class notes and the reference material listed on the web-page and/or during lectures for solving assignment problems. Please do not collaborate. Please cite all the references used for solving each of the problems. All assignments need to be submitted electronically using the brightspace system.

Assignment #
Due Date

Oct 04


Nov 08

Dec 03

- Test: 20%
: Scheduled during the class time slot on November 1st.

- Final Exam: 44% Scheduled by the Exam Services

Note: Final exam will consist of several problems from various topics in the course. Questions will be similar to what you have seen in the assignments. The problems will use the ideas directly from the lectures. Please review the references, class notes, and problems mentioned in the assignments and/or notes. For each topic covered in the class - try to recall the main idea, the primary technique, and how the stated performance bounds were derived.

Schedule for FALL 2023

Important Considerations:

Late assignments are not accepted. Assignments submissions are handled electronically and there is no "grace period" with respect to a deadline. Technical problems do not exempt you from this requirement. You are advised to:

Undergraduate Academic Advisor:

The Undergraduate Advisor for the School of Computer Science is available in Room 5302 HP; or by email at  The undergraduate advisor can assist with information about prerequisites and preclusions, course substitutions/equivalencies, understanding your academic audit and the remaining requirements for graduation. The undergraduate advisor will also refer students to appropriate resources such as the Science Student Success Centre, Learning Support Services and Writing Tutorial Services.

University Policies

We follow all the rules and regulations set by Carleton University, Dean of Science, and the School of Computer Science regarding accommodating students with any kind of need(s). Please consult with the appropriate authorities to see how you can be accommodated and please follow their procedures. For information about Carleton's academic year, including registration and withdrawal dates, see Carleton's Academic Calendar. Following is a standard list of recommendations that we have been advised to provide you with respect to whom to contact for the appropriate action(s):

Pregnancy Obligation. 
Please contact your instructor with any requests for academic accommodation during the first two weeks of class, or as soon as possible after the need for accommodation is known to exist. For more details, visit Equity Services.

Religious Obligation. 
Please contact your instructor with any requests for academic accommodation during the first two weeks of class, or as soon as possible after the need for accommodation is known to exist. For more details, visit Equity Services.

Academic Accommodations for Students with Disabilities
If you have a documented disability requiring academic accommodations in this course, please contact the Paul Menton Centre for Students with Disabilities (PMC) at 613-520-6608 or for a formal evaluation or contact your PMC coordinator to send your instructor your Letter of Accommodation at the beginning of the term. You must also contact the PMC no later than two weeks before the first in-class scheduled test or exam requiring accommodation (if applicable). After requesting accommodation from PMC, meet with your instructor as soon as possible to ensure accommodation arrangements are made. For more details, visit the Paul Menton Centre website.

Survivors of Sexual Violence. 
As a community, Carleton University is committed to maintaining a positive learning, working and living environment where sexual violence will not be tolerated, and survivors are supported through academic accommodations as per Carleton's Sexual Violence Policy. For more information about the services available at the university and to obtain information about sexual violence and/or support, visit:

Accommodation for Student Activities.
 Carleton University recognizes the substantial benefits, both to the individual student and for the university, that result from a student participating in activities beyond the classroom experience. Reasonable accommodation must be provided to students who compete or perform at the national or international level. Please contact your instructor with any requests for academic accommodation during the first two weeks of class, or as soon as possible after the need for accommodation is known to exist. For more details, see the policy.

Student Academic Integrity Policy. 
Every student should be familiar with the Carleton University student academic integrity policy. A student found in violation of academic integrity standards may be awarded penalties which range from a reprimand to receiving a grade of F in the course or even being expelled from the program or University. Examples of punishable offences include: plagiarism and unauthorized co-operation or collaboration.

Plagiarism. As defined by Senate, "plagiarism is presenting, whether intentional or not, the ideas, expression of ideas or work of others as one's own". Such reported offences will be reviewed by the office of the Dean of Science. More information and standard sanction guidelines can be found here:

Unauthorized Co-operation or Collaboration.
Senate policy states that "to ensure fairness and equity in assessment of term work, students shall not co-operate or collaborate in the completion of an academic assignment, in whole or in part, when the instructor has indicated that the assignment is to be completed on an individual basis". For this course, the following holds:

Important Dates: For important academic dates and deadlines, refer to  Carleton's Academic Calendar.

Announcements: Please attend classes to know any course related announcements.