685.621.81 - Algorithms for Data Science
Description
This course offers an in-depth journey through the algorithmic concepts vital for mastering the intricacies of data science. It begins with an intensive examination of algorithm analysis, with a special focus on understanding the runtime complexities essential for addressing real-world data problems. The curriculum encompasses thorough training in data preprocessing, along with foundational knowledge in probability and statistics, equipping students to proficiently clean and interpret data. The course introduces key mathematical transformations such as Eigen decomposition, FFT, DCT, and Wavelets. These tools are crucial for unearthing underlying patterns in data by creating innovative feature spaces. Students will explore a seamless blend of diverse algorithm types, including intelligent algorithms, statistical algorithms, optimization algorithms, graph algorithms, and learning algorithms. This comprehensive approach, enriched with optimization techniques, forms a holistic toolkit for the contemporary Data Scientist. Moving beyond theoretical concepts, the course delves into practical aspects of analysis, visualization, and understanding of complexity classes. Occasional forays into algorithmic proofs enhance the theoretical grounding of students, bridging theory with practical application. The course culminates in modules focused on data modeling and visualization, enabling students to adeptly apply algorithmic techniques to produce insightful and meaningful data representations. Upon completing this course, students will be thoroughly equipped with both practical and theoretical algorithmic strategies, preparing them to confidently address a wide array of challenges in the data science field. Students can only earn credit for one of EN.605.620, EN.605.621, or EN.685.621.
Instructors
Course Structure
The course materials are divided into 14 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. The modules run for a period of seven (7) days, a wrap-up of each of the modules is contained in the Course Outline listed under the Course Information by clicking Home on the course menu. You should regularly check the Calendar and Announcements for assignment due dates. If you are taking the course in the Summer, please note that Modules 1 and 2 are combined as well as Modules 13 and 14 to ensure all 14 modules are covered during the 12 weeks for the Summer semester.
Course Topics
- Introduction to Algorithms
- Basic Algorithm Analysis
- Probability Distributions
- Data Processing
- Data Transformations
- Artificial Intelligence
- Computational Statistics
- Graph Algorithms
- Game Theory
- Machine Learning
- Neural Networks
- Optimization
- Deep Learning
- Data Modeling and Data Visualization
- NP-Completeness and Approximation Algorithms
Course Goals
The course goal is 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.
Textbooks
Not required:
Thomas H. Cormen, Charles E. Leiserson, Ronal L. Rivest and Clifford Stein, Introduction to Algorithms, 4th Edition, MIT Press, 2022.
Stuart J. Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, 4th Edition, Prentice Hall, 2020.
Other Materials & Online Resources
Christopher Bishop, Pattern Recognition and Machine Learning, Springer, 2006, https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop- Pattern-Recognition-and-Machine-Learning-2006.pdf.
- David Poole and Alan Mackworth, Artificial Intelligence Foundations of Computational Agents, Cambridge, 2017, https://artint.info/
Ian Goodfellow, Yoshua Bengio and Aaron Courville, Deep Learning, MIT Press, 2016, https://www.deeplearningbook.org/.
Mykel J. Kochenderfer and Tim A. Wheeler, Algorithms for Optimization, MIT Press, 2019, https://algorithmsbook.com/optimization/files/optimization.pdf.
Required Software
In this class, examples will be given in Matlab (consider the Matlab code provided to be executable psuedocode). In line with the Data Science and Artificial Intelligence programs, you will be required to implement all assignments in this course in python unless otherwise stated.
Student Coursework Requirements
Grading consists of
- 10% for Module Review Questions
- 30% for Homework Assignments
- 40% for Programming Assignments
- 20% Module Activity/Discussion Board
Grading Policy
In this course we do not grade on a curve.
EP uses a +/- grading system (see “Grading System”, Graduate Programs catalog, p. 10).
| Score Range | Letter Grade |
|---|---|
| 100-98 | = A+ |
| 97-94 | = A |
| 93-90 | = A− |
| 89-87 | = B+ |
| 86-83 | = B |
| 82-80 | = B− |
| 79-77 | = C+ |
| 76-73 | = C |
| 72-70 | = C− |
| 69-67 | = D+ |
| 66-63 | = D |
| <63 | = F |
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.