Introduction to Machine Learning
Ended May 6, 2021
Spots remaining: 14
Full course description
Term: Spring 2021
Dates: January 29th, February 12th, 26th, March 12th, 26th, April 9th, 23rd, & May 6th, 2021
Time: 3:00 - 5:00pm each session
Location: ONLINE ONLY
Instructors: Frances McCarty, Jennifer Van Mullekom, & Kate Miller
Presented By: Statistical Applications &. Innovations Group (SAIG)
8 week course (meets every other week) each session is 2 hours
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.
ML Learning Objectives:
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
Attend this course on [date] from [time] in [location]. Familiarity with R/Python is required.
**Prerequisites: Simple Linear Regression and Model Selection in R
Bring your laptop and please install R and RStudio on your machine by downloading from the following links:
Or you can instead install the latest version of Python and your favorite IDE on your machine by downloading from the following links:
https://www.anaconda.com/products/individual (Package manager including both Python and a number of popular IDE’s)
For both platforms, make sure your versions are the most up to date.
This course is jointly sponsored by the Statistical Application and Innovations Group (SAIG) and Professional Development Network (PDN).