Statistics 2020
MAT 502 - Advanced Statistics
Lecture: 4:00-5:30 PM Mon and Fri, GICT 111
Instructor: Shashi Prabh
Email: shashi.prabh@ahduni
Office: GICT 131
Office hour: 4-5 PM Tuesdays, or by appointment
Prerequisites
MAT202 Probability and Random Processes, Ability to write code
Description
This course will introduce some of the fundamental concepts of statistics and statistical methods. One of the goals of this course is to discuss applications of statistical methods to solve real-world problems.
The course is organized into three parts. The first part introduces descriptive statistics, probability, distributions and convergence of random variables. The second part introduces sampling, experiment design and resampling methods. The third part introduces inference and learning from data where it covers topics such as parametric inference, hypothesis testing, statistical learning, regression and classification. The course will have two projects where the first project gives the students a taste of real-world data collection and statistical analysis, and the second project gives experience with statistical learning using real-world datasets. Students will use software to implement and experiment with the concepts taught in the course.
Learning outcomes
Students will be able to
Select and apply appropriate statistical method to a given real-world problem
Use R or Python for data analysis
Gain understanding of the statistical principles that are the basis of machine learning (learning from data)
Course content
Introduction, descriptive statistics , Probability, Bayes theorem and applications, Random variables, Distributions, Marginals, Multivariate distributions, Expectations, MGFs, Convergence of random variables, Sampling theory, Confidence intervals, Experiment design, Resampling methods, Parametric inference, Hypothesis testing, Comparing two populations , Statistical learning, Linear regression, Logistic regression, LDA, KNN.
Books
Statistics, Freedman, Pisani and Purves, 4th edition, W. W. Norton, 2014
All of statistics, Wasserman, Springer, 2003
An Introduction to Statistical Learning with Applications in R, James, Witten, Hastie and Tibshirani, Springer, 2017
Authors have made the book available online here.
An Introduction to R, Venables and Smith, available online at: https://cran.r-project.org/
Grading
Quizzes: 10%
Assignments: 5%
Mid-term exam: 35%
Final exam: 45%
Projects: 5%
Helpful Advice (Expectation from students)
Pay attention and take notes! Get doubts cleared during the lecture itself -- do not hesitate to ask questions in class. Before coming to a lecture review your notes and scan the portion of the textbook that will be covered (see the course calendar page here). Do assignments on your own. If you happen to miss some session(s), do talk to someone in the class or the TA to find out the topics covered and any announcement made.