Advanced statistical modeling: Introduction to Machine learning
Date & Time
In this webinar we will introduce basic concepts behind the machine learning algorithms, their difference from the statistical methods and the most common type of problems that can be tackled with machine learning algorithms.
In this webinar you will learn about:
- Statistical learning versus machine learning
- Supervised versus non-supervised learning
- Data clustering with k means
- Supervised learning with K
- Nearest neighbors
- Foundational concepts of predictive power of the model using cross validation, and use of train and test data
- Foundational concepts behind the use of smoothing curves to fit non-linear curves with loess
This webinar series was made possible via joint collaboration between Faculty of Science (FOS), Applied Statistics and Data Science Group (ASDa) and Graduate Pathways for Success Program (GPS), funded by the UBC Science Strategic Innovation Fund.
Statistics is a scientific discipline that enables reaching meaningful conclusions from data. To produce reliable results, you need to justify the choice of the applied statistical methods and models as well as validate the underlying assumptions.
This series of two 2-hour webinars provide introductions to the foundational concepts of advanced statistical methodologies in non-linear models and machine learning algorithms. We will discuss different statistical models and machine learning methods, their appropriate application and how to interpret the results obtained from them. The aim is to equip the attendees with a deeper understanding of the key concepts of statistical and machine learning methodology, rather than solving specific project problems. While R code for hands-on guidance is provided the focus of this series will be on the methodological aspects.
Each webinar is a self-contained introduction to different advanced statistical concepts, but as topics become increasingly complex with each consecutive webinar, some aspects will be built on concepts taught in the previous sessions including the webinar series on foundational concepts. Hence, there is benefit in attending all the webinars.
If you are a graduate student and have questions about your specific project, please see the SOS Program to book a one-hour free statistical consultation.
The Applied Statistics and Data Science Group (ASDa) in the UBC Department of Statistics provides statistical consulting services and participates in collaborative research. ASDa expertise includes problem formulation, translation of research questions into testable statistical hypotheses, design of experiments and sampling plans for surveys, the choice and explanation of statistical methodology, statistical computing and graphics, the interpretation of findings and more. ASDa also plays an active role in continuing education on and off the UBC campus, giving seminars, webinars, hands-on workshops and courses on statistical concepts and methodologies to various departments, research groups and at teaching hospitals.
Nikolas Krstic is a PhD graduate student at the Department of Statistics and a part-time Statistical Consultant with ASDa. While pursuing his previous degrees, he worked as a statistical analyst at the British Columbia Centre for Disease Control (BCCDC), authoring several published papers on a wide range of environmental health topics. Over the past couple of years, he has worked with numerous clients on projects from a variety of different disciplines. During his studies, research and consulting work, he has developed a strong background in regression analysis, spatial statistics and statistical learning.
General registration opens on Tuesday, February 21st at 9:00 AM.
Priority will be given to UBC graduate students registered in the current academic session. After registering, you will receive confirmation and additional event details within 2 - 3 business days at the e-mail associated with your community.grad.ubc.ca account. If you experience any difficulty using the online registration tool, please e-mail us at firstname.lastname@example.org.
Please email us if you are registered and no longer able to attend this event.