COMPSCI 514: Algorithms for Data Science (Fall 2023)

Time: Tuesday/Thursday 1:00pm-2:15pm
Location: Goessmann Laboratory. Room 20. Lectures will be recorded and posted along with annotated slides under the Schedule tab.
Professor: Cameron Musco
Teaching Assistants:
Course Description: With the advent of social networks, ubiquitous sensors, and large-scale computational science, data scientists must deal with data that is massive in size, arrives at blinding speeds, and often must be processed within interactive or quasi-interactive time frames. This course studies the mathematical foundations of big data processing, developing algorithms and learning how to analyze them. We explore methods for sampling, sketching, and distributed processing of large scale databases, graphs, and data streams for purposes of scalable statistical description, querying, pattern mining, and learning. 3 credits.
Prerequisites: The undergraduate prerequisites are COMPSCI 240 or STAT 515 (Probability) and COMPSCI 311 (Algorithms). This is a theoretical course with an emphasis on algorithm design, correctness proofs, and analysis. Aside from a general background in algorithms, a strong mathematical background, particularly in linear algebra and probability is required. If you are a masters student with a limited background in either of these subjects, please email me at the start of the semester to discuss your preparation.
Textbooks: This is no official textbook for this class. We will use some material from: Readings from these books and other sources will be posted before class under the Schedule tab.
Related Classes: You may also find some helpful reference material in these similar classes taught at other universities:
Piazza: We will use Piazza for class discussion, questions, and annoucements. Sign up here. We hope for Piazza to be a key interactive component of the class. Thus, we encourage posting and good answering of other students' questions as part of up to 5% extra credit for class participation (see below).
Grade Components:
Grade Scale: The course is graded on a standard scale. I will typically shift this scale down to account for any difficult exams/problems sets. I will never shift it up. I.e., if you obtain a 90% in the course, you will definitely achieve an A-, and potentially an A. If applicable, I will publish the shifted scale at the conclusion of the course. The standard grade scale is: A (100-93), A- (92-90), B+ (89-87), B (86-83), B- (82-80), C+ (79-77), C (76-73), C- (72-70), D+ (69-67), D (66-63), D- (62-60), F (below 60).
Problem Sets: The problem sets will be split into two components: core competency questions and challenge questions.
Problem Set Submissions: Problem sets can be completed in groups of up to three students. If you work in a group, you submit a single problem set together. You may talk to people not in your group about the problem sets at a high level, but may not work through the detailed solutions together, write them up together, etc. We very strongly encourage you to work in a three person group, as it will give an advantage in doing the problem sets. At the beginning of the semester we will make a Piazza post where you can look for teammates.
Weekly Quizzes: A quiz will be posted in Moodle each Thursday after class, due the following Monday at 8pm. These are short quizzes (designed to take ~15 minutes) to check that you are following the material and help me make adjustments if needed. Quizzes will include check-in questions asking for feedback on class pacing and on topics that need clarification, or that you would like to see discussed more. While we will not allow any excused misses of quizzes, the lowest quiz grade will be dropped, so that each student can miss one quiz during the course of the semeseter without it affecting their grade.
Exams: The midterm will be held in class on Tuesday October 24th, and the final will held on Thursday December 14th from 10:30am-12:30pm (also in Goessmann Lab, Room 20). Both will be closed notes. We will post extensive review material, past exams, and other practice questions to help you prepare. There is no option to take the exams remotely. Any makeup exams needed due to illness or other excused absences will be held in person.
Class Participation: Up to 5% extra credit may be awarded for class participation. This may come in many forms, e.g.:
Course Academic Honestly Policy: If caught violating the problem set or quiz rules, students will receive a 0% on the assignment for the first violation, and fail the class for a second violation. Any cheating on the midterm or final will lead to failing the class. For fairness, we apply these rules universally, without exceptions.
UMass Academic Honesty Statement: Since the integrity of the academic enterprise of any institution of higher education requires honesty in scholarship and research, academic honesty is required of all students at the University of Massachusetts Amherst. Academic dishonesty is prohibited in all programs of the University. Academic dishonesty includes but is not limited to: cheating, fabrication, plagiarism, and facilitating dishonesty. Appropriate sanctions may be imposed on any student who has committed an act of academic dishonesty. Instructors should take reasonable steps to address academic misconduct. Any person who has reason to believe that a student has committed academic dishonesty should bring such information to the attention of the appropriate course instructor as soon as possible. Instances of academic dishonesty not related to a specific course should be brought to the attention of the appropriate department Head or Chair. Since students are expected to be familiar with this policy and the commonly accepted standards of academic integrity, ignorance of such standards is not normally sufficient evidence of lack of intent.
Disability Accommodations: The University of Massachusetts Amherst is committed to providing an equal educational opportunity for all students. If you have a documented physical, psychological, or learning disability on file with Disability Services (DS), you may be eligible for reasonable academic accommodations to help you succeed in this course. If you have a documented disability that requires an accommodation, please notify me within the first two weeks of the semester so that we may make appropriate arrangements.
I understand that people have different learning needs, home situations, etc. If something isn’t working for you in the class, please reach out and let’s try to work it out.
Helpful UMass Resources:
Learning Objectives: