685.801.21 - Independent Study in Data Science I

Expanded Course Description

In this course the following will be covered to give students the opportunity to cover various areas of the Computer Science and Data Science programs: - As a starting point an initial investigation of the data is conducted to determine what is the data, the basic information is from the website (http://www.gbsense.net/challenge/) 

 - The basic challenge: 1-signal modulation recognition - There exists one signal of several MHz bandwidth and an unknown central frequency between [-600, 600] MHz. The task is to identify the modulation of the signal from sub-Nyquist samples of the signal. The participating teams are required to train the model through the training dataset to realize the identification and verify their model with the test dataset. 
- The advanced challenge: 2-signal modulation recognition - There are two signals of several MHz bandwidth and different central frequencies between [-600, 600] MHz. The positions of the signals are indexed by evenly dividing [-600, 600] MHz into 24 sub-bands. The datasets are given with labels of positions and modulations of both signals. The participating teams are required to train the model through the training dataset to identify both modulation types of the signal and verify their model with the test dataset.

 - A module or two from the Advance Signal Processing class is provided to the students which shows the components of signal processing techniques. The signals in one dimension will be expanded to multiple dimension using Wavelets, FFT and DCT allowing the characteristics for the signal to be expanded for a deep learning technique. - Data Science components to process the data will be used, e.g., normalization, generating time-dependent signals/features, analyzing the expanded signals to determine class separability, etc.  - Computer science components will be used to follow a software process and standards to represent JHU EP well. A module will be presented on the best SW practices and processes to ensure the students understand how we will conduct our development.   - A Deep-Learning (DL) framework will be set up, it is recommended to the students in the course that the Keras API be used to ensure a solid framework is used.  - The baseline optimization method known as the Adam optimizer is to be used, great documentation and the recommended optimizer. The team will work to improve an optimizer and compare with Adam. - The unique component for the class and the challenge will be to use Reinforcement Learning (RL) to select the Layers, Nodes and Activation functions. This has not been done before, an introduction to RL will be covered in a module to show how the Layers and Nodes as the states with the Activation functions as the actions. The reward would be grounded with the Metric in Keras, most likely Classification metrics based on the T/F positives and negatives. - If the challenge does not allow the the training and verification data to be used for publication, the data set from Kaggle web page (https://www.kaggle.com/datasets/zihangsong/gbsense-2022-data?resource=download ) will be used to publish an end of semester conference paper.   - Repository will be GitHub, work environment with Google Colab, Overleaf for documentation in LaTeX. 

Course Structure

Each week a new module will be uploaded to help the students.

Course Topics

Digital Signal Processing
Digital Communications
Reinforcement Learning
Deep Learning
Data Preprocessing

Course Goals

Develop a new method to trining a deep learning techniques using Reinforcement Learning techniques. 

Textbooks

Textbook will not be required, rather material will be uploaded into Canvas as modules. 

Required Software

Python
Google Colab Pro
Github

Student Coursework Requirements

Grading will be based on the final code and paper as follows:
Code - 50%
Conference Paper - 50%

Grading Policy

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. 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. Our courses are designed with a proactive approach to accessibility to minimize the need for disability disclosure and accommodation requests, but we recognize that you may need additional support. 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 EP Student Disability Services at 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 Student Conduct Code website.

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.