MA506 Probability and Statistical Inference / Fall 2022

Course Instructor: Prashant Shekhar, PhD

Contact:

• Email: prashant.shekhar@erau.edu
• Office: Bldg COAS Rm. 301.26

Class Details

• Class time: MWF 5:00 PM to 5:50 PM
• Class venue: Bldg COAS Rm. 316
• Office hours: MWF 1:00 PM to 2:00 PM
• Office hours Venue: Bldg COAS Rm. 301.26
• Course description (pdf)
• Tentative schedule (pdf)

Course Description

This course will focus on using ideas from Probability and Statistics to solve problems in data science. The main topics included in the course are 1. Fundamentals of Statistical Learning 2. Linear Regression 3. Classification 4. Statistical Model Selection 5. Advanced topics in Regression/Classification

This course will focus on using ideas from Probability and Statistics to solve problems in data science. The main topics included in the course are

1. Fundamentals of Statistical Learning
2. Regression
3. Classification
4. Statistical Model Selection

The concepts that you learn in this course can be utilized to solve problems in the general area of machine learning and data science. The applications encompass multiple domains including healthcare, management, manufacturing, security and remote sensing etc.

Text Book

The study material for the course would be provided to the students in the form of jupyter notebooks, pdfs and handwritten notes. Additionally, students are encouraged to refer the textbooks menntioned below for a much deeper understanding

1. James, Gareth, et al. An introduction to statistical learning, with Applications in R, Second Edition, 2021.
2. Ruppert, David, Matt P. Wand, and Raymond J. Carroll. Semiparametric regression. No. 12. Cambridge university press, 2003.
3. Hastie, Trevor, et al. The elements of statistical learning: data mining, inference, and prediction. Vol. 2. New York: springer, 2009.

Attendance

I will try to take attendance in every class and I encourage you to participate in class activities. This is because attendance is found to be heavily correlated with the course grade and attending class everyday ensures that you will not miss any important announcement.

Grading

Your course grade will be determined as follows:

1. Homeworks: 40%
2. Tests: 30%
3. Class participation and attendance: 5%
4. Final (project and presentation): 25%

The grading is expected to follow the standard scale

• A: 90% - 100%
• B: 80% - 89.5%
• C: 70% - 79.5%
• D: 60% - 69.5%
• F: <60%

However, based on the performance of the entire class, I might curve the grading scale later.

Test

You will have 2 tests in the course. Make-ups on the test may be allowed only for valid extenuating circumstances when I am informed before the test takes place – please see me about conflicts as soon as they occur.

Project and Presentation

During the semester, you will be supervised to work on a project which combines classroom materials and real-world applications. The project together with the presentation is the final deliverable for the course. It is supposed to be a group project with teams consisting of 2–4 students. I will work with each of the team separately to identify a topic of your interest and find a relevant project in that domain. In case you are already working on a research problem related to the topics discussed in class, that can also be considered. I will announce project guidelines and rubric in due course.

Homeworks

Your homework grade will be determined based on 4 programming oriented homeworks. You are required to use Python (Jupyter notebooks) to solve homework problems. These exercises will test the ability of the students to apply the concepts in statistical learning on various categories of data sets.

Academic Integrity

Embry-Riddle Aeronautical University maintains high standards of academic honesty and integrity in higher education. To preserve academic excellence and integrity, the University prohibits academic dishonesty in any form, including, but not limited to, cheating and plagiarism. More specific definitions of these violations and their consequences are described in detail in the Dean of Students’ Honor Codes and Student Policies.

Disability Services

DSS Administration Office: Bldg 500; Contact: (386) 226-7916; email: dbdss@erau.edu

1. Student Disability Services: Students with disabilities who believe that they may need accommodations in this class are encouraged to contact the Office of Disability Services. Professors cannot make appropriate disability accommodations. Students are encouraged to register with DSS at the beginning of the term to better ensure that such accommodations are implemented in a timely fashion. Accommodations are not granted until official notice is received from DSS.
2. It is the responsibility of the student to notify DSS the date and time of test once s/he has been made aware of the scheduled test. DSS requires: (a) 2 business days minimum notification for tests and quizzes and (b) 5 business days minimum notification for final exams. Professors cannot make appropriate testing modification without notification from DSS.