Course Dates: August 16th 9:00am - 11:00am & 1:00pm -3:00pm, August 17th 9:00am - 11:00am & 1:00pm -3:00pm, August 18th 9:00am - 11:00am & 1:00pm -3:00pm, and August 19th from 9:00am-11:00am.
Who This Course Is For
Machine learning and data science methods have recently been co-opted into virtually all fields of study. These methods have become an integral part of the toolkit of tomorrow’s worker. Even if you’re not studying statistics, computer science or math, we can guarantee that these tools will be useful for whatever endeavor you plan to undertake in today’s modern economy. In this short course, we will go through an introduction of machine learning methods, both introducing the fundamental concepts underlying the most popular algorithms and showing how to employ these methods to derive meaningful conclusions from data. This course includes both hands-on, in-class exercises and take home practice exercises for students to sharpen their understanding of the course material. Material is provided in both R and Python.
Duration: 4 day short course
August 16th 9am - 11am & 1pm -3pm,
August 17th 9am - 11am & 1pm -3pm,
August 18th 9am - 11am & 1pm -3pm,
August 19th from 9:00am-11:00am.
Venue: Virtual Only
Required Software: R & R Studio (free)
Cost: Free to VT Participating Colleges and Administrative Units
Pre-requisites: Simple Linear Regression and Model Selection in R. Familiarity with R/Python is required. Knowledge of basic descriptive statistics is assumed.
Prework includes downloading course materials, R, and R Studio.
By the end of this course you will:
- Students will learn the concepts behind the prevailing machine learning algorithms
- Students will learn how to code the algorithms in both R and Python, as well as how to employ these algorithms in the process of data-based decision making
- Students will learn where to find more information about the latest developments in machine learning
- Students will learn where to find assistance in writing code for machine learning algorithms
This course includes both hands-on, in-class exercises and take home practice exercises for students to sharpen their understanding of the course material. Material is provided in both R and Python.