Web-Based Lectures




Title: An Introduction to R for Non-Programmers
Presenter: Dr. William Franz Lamberti
Dates and Times: This will be a three-part webinar presentation from 11:00 a.m. – 1:00 p.m. Eastern time on September 7th, 8th, and 9th. Register just once to receive the access information for all three sessions.

Registration Deadline: Monday, September 6, at 12:00 p.m. Eastern time

Description:
In this one day course, participants will be introduced to the basics of R. Basic data manipulation, cleaning, and data visualization will be discussed. This course satisfies the needed prerequisites for Machine Learning Foundations: A Hands-on Introduction Learning (sponsored by ASA October 5-7 or https://vimeo.com/ondemand/mlintro). Learning through examples will be greatly emphasized. This course will be conducted virtually, so participants are highly encouraged to attend with "R-ready" computers (Windows, Mac, or Linux are all acceptable) to work on examples during the course. This course is designed for individuals who have little to no experience with object oriented programming. Familiarity with programming in tools such as SAS will be helpful, but is not required. It is assumed that the baseline familiarity with data analysis tools have been primarily through a graphical user interface such as Excel.

Outline:
Day 1: 9/7/21: 11 AM EST
Hour 1: R is a Big Fancy Calculator - Topics include vectors, matrices, and math operations.
Hour 2: Computing Things Quickly - Topics include functions and computing linear regression.

Day 2: 9/8/21: 11 AM EST
Hour 1: Dealing with Data - Topics include objects, object types, and data frames.
Hour 2: Plotting Data Part 1 - Topics include scatterplots, color, and titles.


Day 3: 9/10/21: 11 AM EST
Hour 1: Plotting Data Part 2 - Topics include histograms and combining plots.
Hour 2: R Packages for Visualization, Discussion, and Questions - Topics include R packages and other resources available. This time will also be allotted for specific questions from the participants. If there is extra time, additional topics such as ggplot2 may be introduced.

Learning Objectives:
By the end of the course, participants will have been introduced to introductory R programming. The material presented will provide the foundations to understand more complicated R functionalities. The material provides the basic knowledge to understand programming at a more general level with the use of text editors.

Content and Instructional Methods:
The course will have slides for the content. However, many hands on examples are done throughout the course. Generally speaking, each hour will end with an example which summarizes the content for that hour. Participants will have an opportunity to work together to solve the example. The hour ends with presenting a solution to the example provided.

Presenter Background:
Dr. Lamberti (Ph.D., Computational Sciences and Informatics with a concentration in Data Science, M.S., Statistical Science) has given talks on R at NASA Langley Research Center, George Mason University’s Aspiring Scientists Summer Internship Program, the American Statistical Association’s (ASA’s) Joint Statistical Meetings, and ASA’s Professional Development Webinar Series. He has also received teaching honors as the 2016-2017 Outstanding Graduate Teaching Assistant at George Mason University Statistics Department. He is currently a Postdoctoral Associate at the University of Virginia. Examples of his talks at George Mason University are found at the following link: http://www.rgalleon.com/talks/

Registration Fees:
ASA Members: $85
Student ASA Member: $60
Nonmembers: $135

This presentation will use the Zoom webinar platform. Each registration is allowed one web connection. Sound is received via audio streaming from your computer’s speakers.

Register

Access Information

Registered persons will be sent an email the afternoon of Monday, September 6, with the information to join the webinars and, if possible, a link to download and print a copy of the presentation slides.



Title: Making Inferences from Non-probability Samples through Data Integration
Presenter: Jean-François Beaumont, Statistics Canada
Date and Time: Tuesday, September 28, 12:00 p.m. – 2:00 p.m. Eastern Time
Sponsor: Survey Research Methods Section

Registration Deadline: Monday, September 27, at 12:00 p.m. Eastern time

Description:
For several decades, national statistical agencies around the world have been using probability surveys as their preferred tool to meet information needs about a population of interest. In the last few years, there has been a wind of change and other data sources are being increasingly explored. Five key factors are behind this trend: the decline in response rates in probability surveys, the high cost of data collection, the increased burden on respondents, the desire for access to “real-time” statistics, and the proliferation of non-probability data sources. In this presentation, I review some data integration approaches that take advantage of both probability and non-probability data sources. Some of these approaches rely on the validity of model assumptions, which contrasts with approaches based solely on the probability sampling design. These design-based approaches are generally not as efficient; yet, they are not subject to the risk of bias due to model misspecification.

Registration Fees:
SRMS Members: $20
ASA Members: $30
Student ASA Member: $25
Nonmembers: $45

Each registration is allowed one web connection. Sound is received via audio streaming from your computer’s speakers.

Register

Access Information

Registered persons will be sent an email the afternoon of Monday, September 27, with the information to join the webinar and, if possible, a link to download and print a copy of the presentation slides.



Title: Machine Learning Foundations: A Hands-on Introduction
Presenter: Dr. William Franz Lamberti
Dates and Times: This will be a three-part webinar presentation from 11:00 a.m. – 1:00 p.m. Eastern time on October 5th, 6th, and 7th. Register just once to receive the access information for all three sessions.

Registration Deadline: Monday, October 4, at 12:00 p.m. Eastern time

Description:
In this course, participants will be introduced to the foundational approaches in machine learning (ML). The emphasis of the course is introducing various algorithms in ML for a variety of tasks such as classification and prediction of continuous values. This course requires the skills presented in An Introduction to R for Non-Programmers (sponsored by ASA September 7-9 or https://vimeo.com/ondemand/rintro). Learning through examples will be greatly emphasized. Data analyzed is related to, but not limited to, Geography, Biology, Meteorology, and Image Analysis. The data is available on base R or GitHub. This course will be conducted virtually, so participants are highly encouraged to attend with "R-ready" computers (Windows, Mac, or Linux are all acceptable) to work on examples during the course. This course is designed for individuals who have little to no experience with ML. Familiarity with R programming is required.

Outline:
Day 1: 10/5/21: 11 AM EST
Hour 1: The Philosophy of Machine and Statistical Learning- Topics include differences between Machine and Statistical Learning in goals, interpretation, and presentation.
Hour 2: Linear Regression - Topics include linear and multiple regression from the perspective of a Machine Learning Analyst.

Day 2: 10/6/21: 11 AM EST
Hour 1: Support Vector Machines - Topics include support vector machines and various kernels such as the polynomial kernel.
Hour 2: Resampling Methods - Topics include random splits and cross-validation.

Day 3: 10/7/21: 11 AM EST
Hour 1: Tree-Based Methods – Topics include regression and classification trees.
Hour 2: LASSO, Discussion, and Questions - Topic is the least absolute shrinkage and selection operator (LASSO). This time will also be allotted for specific questions from the participants. If there is extra time, additional topics such as neural networks may be introduced.

Learning Objectives:
By the end of the course, participants will have been introduced to popular ML techniques. The material presented will provide the foundation to understand more complicated ML algorithms. The material provides the basic knowledge to learn about advanced techniques.

Content and Instructional Methods:
The course will have slides for the content and are available for the participants. However, many hands on examples are done throughout the course. Generally speaking, each hour will end with an example which summarizes the content for that hour. Participants will have an opportunity to work together to solve the example. The hour ends with presenting a solution to the example provided.

Presenter Background:
Dr. Lamberti (Ph.D., Computational Sciences and Informatics with a concentration in Data Science, M.S., Statistical Science) has given talks on R at NASA Langley Research Center, George Mason University’s Aspiring Scientists Summer Internship Program, the American Statistical Association’s (ASA’s) Joint Statistical Meetings, and ASA’s Professional Development Webinar Series. He has also received teaching honors as the 2016-2017 Outstanding Graduate Teaching Assistant at George Mason University Statistics Department. He is currently a Postdoctoral Associate at the University of Virginia. Examples of his talks at George Mason University are found at the following link: http://www.rgalleon.com/talks/

Registration Fees:
ASA Members: $85
Student ASA Member: $60
Nonmembers: $135

This presentation will use the Zoom webinar platform. Each registration is allowed one web connection. Sound is received via audio streaming from your computer’s speakers.

Register

Access Information

Registered persons will be sent an email the afternoon of Monday, October 4, with the information to join the webinars and, if possible, a link to download and print a copy of the presentation slides.