CSE 390, Fall 2017: Probability & Statistics for Data Science
09/19: Lecture 6 slides and py scripts posted.
09/14: Lecture 5 slides and py scripts posted.
09/12: Lecture 4 slides posted.
08/29: Lecture 1 slides posted.
08/05: Our first lecture will be on Aug 29th (Tues) at 4pm in Frey 205.
CSE 390: Probability & Statistics for Data Science
When: Tue Thu, 4:00pm - 5:20pm
Where: Frey Hall 205
Instructor: Anshul Gandhi
Instructor Office Hours: Tue 3-4pm and Thu 5:30-6:30pm
347, New CS building
Course TA: Caitao Zhan, Kunal Shah
TA Office Hours: By appointment (please email the TA(s) to schedule)
This undergraduate-level special topics course covers probability and statistics topics required for data scientists to analyze and interpret data.
The course will involve theoretical topics and some programming assignments.
The course is targeted primarily for junior and senior undergraduate students who are comfortable with concepts relating to probability and are comfortable with basic programming. Undergraduates from Computer Science, Applied Mathematics and Statistics, and Electrical and Computer Engineering would be well suited for taking this class.
Topics covered include Probability Theory, Random Variables, Stochastic Processes, Statistical Inference, Hypothesis Testing, Regression, Classification, and Clustering. For more details, refer to the syllabus
The class is expected to be interactive and students are encouraged to participate in class discussions.
Grading will be on a curve, and will tentatively be based on assignments, exams, and class participation. For more details, refer to the section on grading
Syllabus & Schedule
|Aug 29 (Tue)
|Course introduction, class logistics
|Aug 31 (Thu)
|Probability review - 1
Basics: sample space, outcomes, probability
Events: mutually exclusive, independent
Calculating probability: sets, counting, tree diagram
AoS 1.1 - 1.6
MHB 3.1 - 3.5
|Sep 05 (Tue)
|Sep 07 (Thu)
|Probability review - 2
Law of total probability
MHB 3.6, 3.10 - 3.11
|assignment 1 out
|Sep 12 (Tue)
|Random variables - 1: Overview
Discrete and Continuous RVs
Mean, Moments, Variance
pmf, pdf, cdf
AoS 2.1 - 2.3
MHB 3.7 - 3.9
|Sep 14 (Thu)
|Random variables - 2: Discrete RVs
MHB 3.7 - 3.9, 3.14.1
|Sep 19 (Tue)
|Random variables - 3: Continuous RVs
Normal(μ, σ2), and its several properties
MHB 3.14.1, 3.10, 3.13
|assignment 1 due
assignment 2 out
|Sep 21 (Thu)
|Sep 26 (Tue)
|Random variables - 4: Joint distributions & conditioning
Joint probability distribution
Linearity (and product) of expectation
Sum of a random number of RVs
MHB 3.11 - 3.12, 3.15
|Sep 28 (Thu)
Weak law of large numbers
Central limit theorem
AoS 4.1 - 4.2, 23.1 - 23.3
MHB 3.14.2, 8.1 - 8.7
|Oct 03 (Tue)
|Non-parametric inference - 1
AoS 6.1 - 6.2, 7.1 - 7.2
||assignment 2 due
|Oct 05 (Thu)
|Non-parametric inference - 2
|Oct 10 (Tue)
- Recommended text: (AoS) "All of Statistics : A Concise Course in Statistical Inference" by Larry Wasserman (Springer publication).
- Students are strongly suggested to purchase a copy of this book.
- Recommended text: (MHB) "Performance Modeling and Design of Computer Systems: Queueing Theory in Action" by Mor Harchol-Balter (Cambridge University Press)
- Suggested for probability review and stochastic processes.
- There is copy placed on reserve in the library. The instructor also has a few personal copies that you can borrow.
- Recommended text: (DSD) "The Data Science Design Manual" by (our very own) Steven Skiena (Springer publication).
- Suggested for data science topics in the second half of the course.
- S.M. Ross, Introduction to Probability Models, Academic Press
- S.M. Ross, Stochastic Processes, Wiley
- Assignments: 48%
- Roughly 6 assignments during the semester. Expect 5-6 questions per assignment, including some programming questions (after mid-term 1).
- Assignments are due in class, at the beginning of the lecture. No late submissions allowed. Hard-copies only, please.
- Exams: 42%
- Two in-class exams.
- Mid-term 1: 20%.
- Mid-term 2: 22%.
- Roughly as difficult as the assignments.
- Class interaction: 10%
- The basic idea is to get you to talk in the class and contribute to discussions.
- By the end of the semester, if I can recognize you based on your contributions to the class discussion, you should get a good score on this.
- Very helpful for bumping your grade if you are on the border.
- Academic dishonesty will immediately result in an F and the student will be referred to the Academic Judiciary. See below section on Academic Integrity.
- Grading will be on a curve.
- Assignment of grades by the instructor will be final; no regrading requests will be entertained.
- There is a University policy on grading, as well as a set of grading guidelines agreed upon by the CS faculty. The instructor is obligated to uphold these policies.
No exceptions will be made for any student and no special circumstances will be entertained.
Each student must pursue his or her academic goals honestly and be personally accountable for all submitted work. Representing another person's work as your own is always wrong. Faculty are required to report any suspected instances of academic dishonesty to the Academic Judiciary. For more comprehensive information on academic integrity, including categories of academic dishonesty, please refer to the academic judiciary website at http://www.stonybrook.edu/commcms/academic_integrity
. Please note that any incident of academic dishonesty will immediately result in an F grade
for the student.
Critical Incident Management
Stony Brook University expects students to respect the rights, privileges, and property of other people. Faculty are required to report to the Office of Judicial Affairs any disruptive behavior that interrupts their ability to teach, compromises the safety of the learning environment, or inhibits students' ability to learn.
Please report any errors to the Instructor.
Disability Support Services
If you have a physical, psychological, medical or learning disability that may impact your course work, please contact Disability Support Services, ECC (Educational Communications Center) Building, room 128, (631) 632-6748. They will determine with you what accommodations, if any, are necessary and appropriate. All information and documentation is confidential. http://studentaffairs.stonybrook.edu/dss