The abridged syllabus you’re looking for has not been entered yet. We’ve provided a similar abridged syllabus as an example. Check back later for that specific abridged syllabus. The complete syllabus will be available in your Canvas course.

605.621.82 - Foundations of Algorithms

Computer Science

Fall 2024

Description

This follow-on course to data structures (e.g., EN.605.202) provides a survey of computer algorithms, examines fundamental techniques in algorithm design and analysis, and develops problem-solving skills required in all programs of study involving computer science. Topics include advanced data structures (red-black and 2-3-4 trees, union-find), recursion and mathematical induction, algorithm analysis and computational complexity (recurrence relations, big-O notation, NP-completeness), sorting and searching, design paradigms (divide and conquer, greedy heuristic, dynamic programming, amortized analysis), and graph algorithms (depth-first and breadth-first search, connectivity, minimum spanning trees, network flow). Advanced topics are selected from among the following: randomized algorithms, information retrieval, string and pattern matching, and computational geometry. Prerequisite(s): EN.605.202 Data Structures or equivalent. EN.605.203 Discrete Mathematics or equivalent is recommended. Course Note(s): The required foundation courses may be taken in any order but must be taken before other courses in the degree. Students can only earn credit for one of EN.605.620, EN.605.621, EN.685.621 or EN.705.621

Expanded Course Description

There are often many ways to solve a computational problem. Can we formalize that some problems are harder than others? Are some problems inherently difficult? How would you determine a “good” solution? Throughout this semester, you’ll explore topics such as:

Algorithm asymptotic analysis and big-O notation,
NP-Complete problems, and their approximate solutions,
Graph algorithms, dynamic and linear programming, and sorting.

Along the way, you’ll build a broad understanding of the process of algorithm analysis - a useful skill when working with cross-disciplinary technical teams.

PYTHON NOTE - The programming assignments use very simple Python. Programming assignments will be given as Jupyter Notebooks, and students will turn in completed Jupyter Notebooks. For students that have not used Python before, it is pretty easy to work with, and I give help along the way, so it's an excellent way to get an intro to this language. If you are coming from Java, C++, or some other language, it's straightforward. If you want a good environment to work in, check out Google Colab. Also, to be clear, the Notebooks contain the structure of the code already, with blocks where the students add their Python code. They also include test blocks so you know your code will at least run when being graded. These Notebooks are not "blank slate" assignments, they have clear places where you fill in your code, meant to be friendly to those new to Python.

Instructor

Russell Fink

russ.fink@jhu.edu

Course Structure

The course materials are divided into modules which can be accessed by clicking Modules on the course menu. A module will have several sections including the overview, content, readings, discussions, and assignments. You are encouraged to preview all sections of the module before starting. Most modules run for a period of seven (7) days, exceptions are noted in the Course Outline. You should regularly check the Grade Center and Announcements for assignment due dates.

Course Topics

Basics of asymptotic notation and analysis
NP-Completeness and problem examples
Randomized Algorithms
Sorting
Data Structures
Optimization Problems and Dynamic Programming
Advanced Data Structures
Graph, Tree Algorithms
Approximation Algorithms

Course Goals

To develop a broad understanding of the issues associated with designing and analyzing the expected performance of computer algorithms, and to develop greater competence and confidence in applying formal mathematical methods when determining the best approach to solving a computational problem.

Course Learning Outcomes (CLOs)

Analyze algorithms to determine worst-case complexity in terms of time and space requirements
Design algorithms to meet functional requirements as well as target complexity bounds in terms of time and space complexity
Develop data structure techniques for various aspects of programming
Implement algorithms to assess their actual performance compared to expectations from analysis

Textbooks

Cormen, Thomas H., Leiserson, Charles E., Rivest, Ronald L., and Stein, Clifford. Introduction to Algorithms, 4th edition. Cambridge: MIT Press, 2022. (Link to book at Sheridan / ProQuestLinks to an external site.)

Student Coursework Requirements

Assignment Type	Grade Percentage
Homework	45%
Programming Assignments	45%
Class Discussion/Collaboration	10%

Grading Policy

Grades should be interpreted according to these general rubrics:

A - complete understanding of the topic plus additional insight, creative, or other indicators of advancement beyond complete understanding of the topic;
B - mastery of the basic material;
C - attempt to complete the basic material but significant gaps in understanding and mastery exist;
D/F -failure to complete basic material successfully.

Late assignments will be penalized the greater of 10 points, or 10 percent of the total grade, per day or portion of a day late. Work submitted later than 5 days will receive a grade of zero.

If you’re at risk of receiving a C or lower in the class, at the midpoint of the semester you’ll receive notice from the university or myself.

EP uses a +/- grading system (see “Grading System”, Graduate Programs catalog, p. 10).

Score Range	Letter Grade
100-97	= A+
96.9-93	= A
92.9-90	= A−
89.9-87	= B+
86.9-83	= B
82.9-80	= B−
79.9-77	= C+
76.9-73	= C
72.9-70	= C−
69.9-67	= D+
66.9-63	= D
<63	= F

Course Policies

Academic Integrity Addendum - In addition to the program and school's guidelines on academic integrity, Dr. Fink considers the following activities in this course to be academically dishonest:

significant inclusion of related material into your submissions, even if properly cited
obtaining direct answers to questions or discussion prompts and submitting as your own work, e.g., from homework assistance sites, Large Language Model / ChatGPT / Bard, paid tutors

For example, if the assignment is to "implement MergeSort," you may not download an implementation from the Internet, revise it, and turn that in. Likewise, you may not import a library that just does it. If you need a snippet that is not central to the assignment, such as code that pretty-prints a list, that is okay as long as it is properly cited. Ask Dr. Fink for clarification if you are unsure.

Academic Policies

Deadlines for Adding, Dropping and Withdrawing from Courses

Students may add a course up to one week after the start of the term for that particular course. Students may drop courses according to the drop deadlines outlined in the EP academic calendar (https://ep.jhu.edu/student-services/academic-calendar/). Between the 6th week of the class and prior to the final withdrawal deadline, a student may withdraw from a course with a W on their academic record. A record of the course will remain on the academic record with a W appearing in the grade column to indicate that the student registered and withdrew from the course.

Academic Misconduct Policy

All students are required to read, know, and comply with the Johns Hopkins University Krieger School of Arts and Sciences (KSAS) / Whiting School of Engineering (WSE) Procedures for Handling Allegations of Misconduct by Full-Time and Part-Time Graduate Students.

This policy prohibits academic misconduct, including but not limited to the following: cheating or facilitating cheating; plagiarism; reuse of assignments; unauthorized collaboration; alteration of graded assignments; and unfair competition. Course materials (old assignments, texts, or examinations, etc.) should not be shared unless authorized by the course instructor. Any questions related to this policy should be directed to EP’s academic integrity officer at ep-academic-integrity@jhu.edu.

Students with Disabilities - Accommodations and Accessibility

Johns Hopkins University values diversity and inclusion. We are committed to providing welcoming, equitable, and accessible educational experiences for all students. Students with disabilities (including those with psychological conditions, medical conditions and temporary disabilities) can request accommodations for this course by providing an Accommodation Letter issued by Student Disability Services (SDS). Please request accommodations for this course as early as possible to provide time for effective communication and arrangements.

For further information or to start the process of requesting accommodations, please contact Student Disability Services at Engineering for Professionals, ep-disability-svcs@jhu.edu.

Student Conduct Code

The fundamental purpose of the JHU regulation of student conduct is to promote and to protect the health, safety, welfare, property, and rights of all members of the University community as well as to promote the orderly operation of the University and to safeguard its property and facilities. As members of the University community, students accept certain responsibilities which support the educational mission and create an environment in which all students are afforded the same opportunity to succeed academically.

For a full description of the code please visit the following website: https://studentaffairs.jhu.edu/policies-guidelines/student-code/

Classroom Climate

JHU is committed to creating a classroom environment that values the diversity of experiences and perspectives that all students bring. Everyone has the right to be treated with dignity and respect. Fostering an inclusive climate is important. Research and experience show that students who interact with peers who are different from themselves learn new things and experience tangible educational outcomes. At no time in this learning process should someone be singled out or treated unequally on the basis of any seen or unseen part of their identity.

If you have concerns in this course about harassment, discrimination, or any unequal treatment, or if you seek accommodations or resources, please reach out to the course instructor directly. Reporting will never impact your course grade. You may also share concerns with your program chair, the Assistant Dean for Diversity and Inclusion, or the Office of Institutional Equity. In handling reports, people will protect your privacy as much as possible, but faculty and staff are required to officially report information for some cases (e.g. sexual harassment).

Course Auditing

When a student enrolls in an EP course with “audit” status, the student must reach an understanding with the instructor as to what is required to earn the “audit.” If the student does not meet those expectations, the instructor must notify the EP Registration Team [EP-Registration@exchange.johnshopkins.edu] in order for the student to be retroactively dropped or withdrawn from the course (depending on when the "audit" was requested and in accordance with EP registration deadlines). All lecture content will remain accessible to auditing students, but access to all other course material is left to the discretion of the instructor.