CSE 391, Spring 2019: Probability & Statistics for Data Science

News:
01/29: Schedule updated.
01/01: Our first lecture will be on Jan 28th (Mon) at 4pm in Frey 217.

CSE 391: Probability & Statistics for Data Science
Spring 2019

When: Mon Wed, 4:00pm - 5:20pm
Where: Frey Hall 217

Instructor: Anshul Gandhi
Instructor Office Hours: Tue 2:30-3:30pm, Thurs 12-1pm; location: 347, New CS building

Course TA: Vamsi and Naimul
TA Office Hours: By appointment (please email the TA(s) to schedule)

### Course Info

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, and Regression. 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 below.

### Syllabus & Schedule

Jan 28 (Mon)
[Lec 01]
Course introduction, class logistics
Jan 30 (Wed)
[Lec 02]
Probability review - 1
• Basics: sample space, outcomes, probability
• Events: mutually exclusive, independent
• Calculating probability: sets, counting, tree diagram
• AoS 1.1 - 1.5
MHB 3.1 - 3.4
Feb 04 (Mon)
[Lec 03]
Probability review - 2
• Conditional probability
• Law of total probability
• Bayes' theorem
• AoS 1.6, 1.7
MHB 3.3 - 3.6
assignment 1 out
Feb 06 (Wed)
[Lec 04]
Random variables - 1
• Mean, Moments, Variance
• pmf, pdf, cdf
• Bernoulli(p)
• Indicator RV
• Binomial(n, p)
• Geometric(p)
• AoS 2.1 - 2.3, 3.1 - 3.4
MHB 3.7 - 3.9
Python scripts:
draw_Bernoulli, draw_Binomial, draw_Geometric
Feb 11 (Mon)
[Lec 05]
Random variables - 2
• Uniform(a, b)
• Exponential(λ)
• Normal(μ, σ2), and its several properties
• AoS 2.4, 3.1 - 3.4
MHB 3.7 - 3.9, 3.14.1
Python scripts:
draw_Uniform, draw_Exponential, draw_Normal
Feb 13 (Wed)
[Lec 06]
Random variables - 3
• Joint probability distribution
• Linearity and product of expectation
• AoS 2.5 - 2.7
MHB 3.10, 3.13
assignment 1 due
assignment 2 out
Feb 18 (Mon) No class Instructor traveling
Feb 20 (Wed)
[Lec 07]
Probability inequalities
• Markov's Inequality
• Chebyshev's inequality
• Weak Law of Large Numbers
• Central Limit Theorem
• AoS 4.1 - 4.2, 5.3 - 5.4
MHB 3.14.2, 5.2
Feb 25 (Mon)
[Lec 08]
Non-parametric inference - 1
• Basics of inference
• Empirical PMF
• Sample mean
• bias, se, MSE
• AoS 6.1, 6.2, 6.3.1 Python scripts:
sample_Bernoulli, sample_Binomial, sample_Geometric

assignment 2 due
assignment 3 out
Required weather.dat dataset for A3.
Feb 27 (Wed)
[Lec 09]
Non-parametric inference - 2
• Empirical Distribution Function (or eCDF)
• Statistical Functionals
• Plug-in estimator
• AoS 6.3.1, 7.1 - 7.2 Python scripts:
sample_Uniform, sample_Exponential, sample_Normal,
eCDF
Mar 04 (Mon)
[Lec 10]
Confidence intervals
• Percentiles, quantiles
• Normal-based confidence intervals
• DKW inequality
• AoS 6.3.2, 7.1
Mar 06 (Wed)
[Lec 11]
Parametric inference - 1
• Basics of parametric inference
• Method of Moments Estimator (MME)
• AoS 6.3.1 - 6.3.2, 9.1 - 9.2 assignment 3 due
Mar 11 (Mon)
Mid-term 1 review
Mar 13 (Wed) Mid-term 1
Mar 25 (Mon)
[Lec 12]
Parametric inference - 2
• Method of Moments Estimator (MME)
• Properties of MME
• AoS 9.1 - 9.2
Mar 27 (Wed)
[Lec 13]
Parametric inference - 3
• Likelihood
• Maximum Likelihood Estimator (MLE)
• Properties of MLE
• AoS 9.3 - 9.4, 9.6 assignment 4 out
Required datasets: q3.dat, acceleration, model, mpg.
Apr 01 (Mon)
[Lec 14]
Hypothesis testing - 1
• Basics of hypothesis testing
• The Wald test
• AoS 10 - 10.1
DSD 5.3 - 5.3.1
Apr 03 (Wed)
[Lec 15]
Hypothesis testing - 2
• The Wald test
• Type I and Type II errors
• AoS 10 - 10.1
Apr 08 (Mon)
[Lec 16]
Hypothesis testing - 3
• t-test
• Kolmogorov-Smirnov test (KS test)
• AoS 10.10.2, 15.4
DSD 5.3.2 - 5.3.3
Apr 10 (Wed)
[Lec 17]
Hypothesis testing - 4
• p-values
• Permutation test
• AoS 10.2, 10.5
DSD 5.5
assignment 4 due
assignment 5 out
Apr 15 (Mon)
[Lec 18]
Hypothesis testing - 5
• Pearson correlation coefficient
• Chi-square test for independence
• AoS 3.3, 10.3 - 10.4
DSD 2.3
Apr 17 (Wed)
[Lec 19]
Bayesian inference - 1
• Bayesian reasoning
• Bayesian inference
• AoS 11.1 - 11.2
DSD 5.6
Apr 22 (Mon)
[Lec 20]
Bayesian inference - 2
• Bayesian inference
• Conjugate priors
• AoS 11.1 - 11.2
DSD 5.6
Apr 24 (Wed)
[Lec 21]
Regression - 1
• Basics of Regression
• Simple Linear Regression
• AoS 13.1, 13.3 - 13.4
DSD 9.1
assignment 5 due
assignment 6 out
Required datasets: q2_sigma3.dat, q2_sigma100.dat, q5.dat.
Apr 29 (Mon)
[Lec 22]
Regression - 2
• Multiple Linear Regression
• AoS 13.5
DSD 9.1
May 01 (Wed)
[Lec 23]
Time Series Analysis
• Last Observed, Seasonal Last Observed
• Simple Moving Average
• EWMA, Holt-Winters
• Autoregression
• May 06 (Mon) Mid-term 2 review Assignment 6 due
May 08 (Wed) Mid-term 2

### Resources

• 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.

• Others:
• S.M. Ross, Introduction to Probability Models, Academic Press
• S.M. Ross, Stochastic Processes, Wiley

• Assignments: 50%
• Roughly 5 assignments during the semester. Expect 5-7 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: 40%
• Two in-class exams.
• Mid-term 1: 15%.
• Mid-term 2: 25%.
• Easier than the assignments and no long derivations or programming questions.
• Make-up exams will only be given at the discretion of the instructor and only for medical emergencies, with required evidence.

• 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.

• Important:
• 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.