Computational Statistics - Spring 2025

This page contains an outline of the topics, content, and assignments for the semester. Note that this schedule will be updated as the semester progresses and the timeline of topics and assignments might be updated throughout the semester.

WEEK	DATE	TOPIC	MATERIALS	CODE	DUE
1	Tue, Jan 14	What to cover in this class?	🔗Syllabus
	Thu, Jan 16	Introduction to Statistical Computing 🎥 Lecture 2	📚Chapt 1-A	R👩‍💻
	Fri, Jan 17	Review Probability and Statistics	💻Review Probability 📖Introduction to Statistics ebook		📝 HW 0 or Learn R in R
2	Tue, Jan 21	⛈ Sever Weather - No Class
	Thu, Jan 23	Probability and Statistics Review 🎥 Lecture 3	📚Chapt 2-A	R👩‍💻
3	Tue, Jan 28	Regression 🎥 Lecture 4	💻Regression	R Shiny👩‍💻
	Thu, Jan 30	Regularization in Regression Method of Estimations 🎥 Lecture 5	💻Ridge and Lasso 📚💻Chapt 2-B
	Fri, Jan 31			D2L✍️	📝 HW 1 at 11:50 pm Solution
4	Tue, Feb 4	Methods ofGenerating Random Vars. 🎥 Lecture 6	📚Chapt 3-A	R👩‍💻
	Thu, Feb 6	More on Generating RV’s 🎥 Lecture 7	📚Chapt 3-B
5	Tue, Feb 11	K-means Clustering 🎥 Lecture 8	💻Clustering-A	R👩‍💻
	Thu, Feb 13	Hirarchical Clustering 🎥 Lecture 9	💻Clustering-B
	Fri, Feb 14			D2L✍️	📝 HW 2 at 11:50 pm Solution
6	Tue, Feb 18	Vizualization 🎥 Lecture 10	📚Chapt 5	R👩‍💻
	Thu, Feb 20	Monte Carlo Integration 🎥 Lecture 11	📚Chapt 6-A	R👩‍💻
7	Tue, Feb 25	Variance Reduction 🎥 Lecture 12	📚Chapt 6-B
	Thu, Feb 27	Importance Sampling and Var. Red. 🎥 Lecture 13 Developing an R package	📚Chapt 6-C 📖R package
	Fri, Feb 28			D2L✍️	📝 HW 3 at 11:50 pm Solution
8	Tue, Mar 4	Developing an Interactive Shiny App 🎥 Lecture 14	📖Shiny	R👩‍💻
	Thu, Mar 6	Object Oriented Programming in R 🎥 Lecture 15	📖OOP	R👩‍💻
	Fri, Mar 7			D2L✍️	📝 HW 4 at 11:50 pm Solution
	Tue, Mar 11	🌴 Spring Break
	Thu, Mar 13	🌴 Spring Break
9	Tue, Mar 18	Monte Carlo Methods 🎥 Lecture 16	📚Chapt 7-A	R👩‍💻
	Thu, Mar 20	More on Monte Carlo Methods 🎥 Lecture 17	📚Chapt 7-B 📚💻Chapt 7-C
10	Tue, Mar 25	Bootstrap 🎥 Lecture 18	📚Chapt 8-A	R👩‍💻
	Thu, Mar 27	More on Bootstrap and Jackknife 🎥 Lecture 19	📑Chapt 8-B 🖥️Chapt 8-C 📚Chapt 9	R👩‍💻
	Fri, Mar 28			D2L✍️	📝 HW 5 at 11:50 pm Solution
11	Tue, Apr 1	Density Estimation 🎥 Lecture 20	📚Chapt 12	R👩‍💻
	Thu, Apr 3	Numerical Methods in R 🎥 Lecture 21	📚Chapt 13-A	R👩‍💻
	Fri, Apr 4			D2L✍️	📝 HW 6 at 11:50 pm Solution
12	Tue, Apr 8	More on Numerical Methods 🎥 Lecture 22 Optimization in R	📚Chapt 13-B
	Thu, Apr 10	More on Optimization 🎥 Lecture 23	📚Chapt 14-A	R👩‍💻
	Fri, Apr 11			[D2L✍️]	📝 [HW 7] at 11:50 pm
13	Tue, Apr 15	Expectation-Maximization Algorithm 🎥 Lecture 24	📑Chapt 14-B	R👩‍💻
	Thu, Apr 17	🐰 Easter Break
14	Tue, Apr 22	Programming Topics 🎥 Lecture 25	📚Chapt 15	R👩‍💻
	Thu, Apr 24	Presentation by Dr. Yu Deep Learning in R 🎥 Lecture 26	💻APST-ADP 💻DLiR-A	R👩‍💻
15	Tue, Apr 29	Fully Connected Networks (FCN) 🎥 Lecture 27	💻DLiR-B
	Thu, May 1	Convolutional Nueral Nets (CNN) 🎥 Lecture 28	💻DLiR-C
16	Mon, May 5	Final Project	📃25 MIN Presentations : 10:30 am - 12:30 pm Evaluation - Group A Evaluation - Group B Evaluation - Group C Evaluation - Group D