SHORT COURSE DESCRIPTION
What is machine learning and how does it compare to statistics and quantitative methods? Machine learning combines methods from computer science and statistics to help researchers to predict data. In addition, machine learning algorithms allow users to analyze datasets that would be difficult or impossible with standard statistical models.
This course will introduce participants to the field of machine learning with specific focus on several standard supervised machine learning algorithms although some unsupervised machine learning algorithms may be introduced as well. Participants are welcome to bring their own data for analysis, but sample datasets will be provided for demonstrations and practice during the workshop. This course will be taught in R. Although all code will be provided, some statistical modeling experience with R is recommended.
DATES AND TIMES
May 25-26, 2023 (Thur-Fri)
10am-5pm Eastern Time (UTC-4)
The instructor will determine timing of lunch break, as well as morning and afternoon breaks.
Full-time student*: $195
*Full-time students need to submit student status proof at https://go.umd.edu/CILVR-STUDENT to request a discount code prior to registration.
*Course fee will be waived for HDQM Department faculty and degree-seeking students, although the UMD IT department will charge you a tech fee to register ($10). HDQM department registrants can request the discount code by submitting the following form: https://go.umd.edu/CILVR-HDQM.
HOW TO REGISTER
Prior to registration, participants not affiliated with UMD need to get a valid UMD associate account in order to register for the short course and access the course content. Participants can visit https://identity.umd.edu/id/associate/registration to create an UMD associate account. For more details about the UMD associate account, please click here.
For UMD affiliated participants, you may register using your existing UMD directory ID.
To request the promotional code prior to registration:
- Full-time students can submit the student status proof at https://go.umd.edu/CILVR-STUDENT to request a student discount code prior to registration. Note that it may take 2-3 business days for your request to be processed.
- HDQM department registrants can request the HDQM discount code by submitting the following form: https://go.umd.edu/CILVR-HDQM. Note that it may take 2-3 business days for your request to be processed.
- Regression models for prediction and classification
- Regularization methods: Lasso, Ridge, Elastic Nets
- Classification trees
- Ensemble tree methods: Bagging, Random forests, Boosting
- Support vector machines
Social science researchers and students without any experience in machine learning.
- Required: Experience with general and generalized linear regression.
- Recommended: Some experience with R.
LOCATION AND PLATFORM
· The course materials and meeting links will be posted on the course page through UMD Open Learning (https://umd.catalog.instructure.com/).
· This workshop will be delivered entirely online via the video conferencing software Zoom (https://zoom.us/).
· Within a limited time, the video recordings of the short course will be accessible for both synchronous and asynchronous participants on the course page.
IMPORTANT COURSE DETAILS
Platform: Participants who are not affiliated with UMD need to get a valid UMD associate ID in order to register for the short course and access the course content. Participants can visit https://identity.umd.edu/id/associate/registration to create an UMD associate account. For more details please click here.
Format: Participants will receive a personalized login code to use on their own computer to access a reliable live-stream of the short course over Zoom, showing the instructor as well as the handouts.
Materials: Participants will receive electronic copies of the short course materials, as well as any other relevant materials or information.
Timing/access: Participants may choose to watch the stream synchronously, or may elect to watch a recording of the short course asynchronously, or both. Recordings will be available to participants for six months following the end of the short course. This is especially useful for on-line participants in different time zones who may choose to watch at some later time than (but within six months of) the actual short course time. (Asynchronous participation does not include real-time chat with other on-line participants, although a visual record of prior chats will be viewable).
Technical support: Participants are responsible for installing the conferencing software Zoom on their own electronic devices and for obtaining a Zoom account that allows the participant to join Zoom meetings and webinars hosted by external organizations. Participants are assumed to be able to secure a reliable computer, internet browser, and Wi-Fi connection. Challenges at the user end must be resolved by the user. Fortunately, because the short course is recorded, users experiencing technical challenges can still “catch up” by watching the recordings to which they have access.
Content support: During the lecture, real-time content support for on-line participants is mostly limited to real-time chat with the on-line (Zoom) participant community and any quantitative methodology doctoral students who might also be participating. Participants may have direct interactions with the instructor in some format during the practice sessions. On-line participants may e-mail the instructor for further content support that cannot be addressed in real-time.
Dr. Tracy Sweet is an Associate Professor of Measurement, Statistics, and Evaluation (EDMS) in the Department of Human Development and Quantitative Methodology at the University of Maryland. Dr. Sweet has taught several workshops on social network models and recently taught an advanced graduate course on social network models. She has also taught a number of graduate-level quantitative methods courses on general linear models, multilevel modeling, Bayesian inference, and statistical consulting. Her research interests include developing statistical models for social networks as well as data mining methods for multilevel social network data. Dr. Sweet has published methodological papers in prominent journals such as Journal of Educational and Behavioral Statistics and Social Networks, and application papers in American Educational Research Journal, Sociology of Education, and Educational Psychologist. Her research has been funded by the Institute of Educational Sciences (IES). She currently serves as an Associate Editor for Journal of Educational and Behavioral Statistics. Before joining the EDMS faculty in the fall of 2013, Dr. Sweet worked as a postdoctoral researcher at Carnegie Mellon University where she received her Ph.D. in 2012. She may be reached at firstname.lastname@example.org.
Full refund if cancellation occurs at least 10 business days prior to the workshop date; 50% refund if within 10 days of the first day of the course.
For any further questions, please contact email@example.com.
To request a copy of the payment receipt, please contact the OES office at firstname.lastname@example.org.
CILVR Short Course Series
Center for Integrated Latent Variable Research (CILVR) at the University of Maryland (UMD)
CILVR is a center whose goal is to serve as a national and international focal point for innovative collaboration, state-of-the-art training, and scholarly dissemination as they relate to the full spectrum of latent variable statistical methods. CILVR is housed within the Measurement, Statistics and Evaluation (EDMS) program in the Department of Human Development and Quantitative Methodology at the University of Maryland. EDMS faculty are recognized scholars in various facets of latent variable statistical models, whether it be item response theory, latent class analysis, mixture models, latent growth models, or structural equation modeling.