Introduction to Machine Learning
Start Date: February 26, 2020
End Date: April 15, 2020
Introduction to Machine Learning is one of five non-credit courses in the Certification in Practice of Data Analytics (CPDA) program. This course can be taken individually or as one of four courses required to receive the CPDA certificate of completion.
Machine Learning is the third course in the sequence of the CPDA program. After learning how to analyze data statistically and the data mining methodology, students now explore the study and construction of algorithms that can learn from and make predictions on data.
The course is taught by faculty from the College of Engineering at The Ohio State University. The course is delivered in 100% distance learning format and includes instructional material equivalent a one semester credit hour class.
In this course students will learn fundamental topics such as nonlinear optimization, dynamic optimization, Markov decision problems, training of deep neural networks, clustering, classification, regression trees, and dimensionality reduction techniques for high dimensional data. The goal of the course is to prepare the student to formulate and solve machine learning problems in multiple domains and access various industries The course will use R extensively for completing all the assignments.
4 CEUs are granted upon successful completion of the course.
You Will Learn to:
- Formulate data-driven machine learning problems.
- Solve unconstrained and constrained optimization problems.
- Differentiate between supervised and unsupervised learning tasks
- Use R to implement various supervised and unsupervised machine learning algorithms.
- Evaluate which learning algorithms are useful for what kind of tasks.
- Assess the limits of machine learning.
- Argue the ethical implications of using machine learning in our daily lives.
Familiarity with calculus, linear algebra (matrices, determinants, eigenvalues, and eigenvectors) convex functions and sets, differentiation of multivariate functions, taylor series for multivariate functions, and basic probability and statistics (random variables, expectation, mean and covariance, characteristic functions, central limit theorem, etc.)
This class requires you to use the statistical software package called R (The R Project for Statistical Computing;(http://www.r-project.org/). This software package is available as Free Software.
Students must complete this free training before starting the Machine Learning class.
- Hands-on tutorials are available in the Swirl system, which you can learn about at http://swirlstats.com/. In particular, “R Programming: The basics of programming in R” is an appropriate first tutorial for students who have never used R.
- An easier to use interface to R is available in the software package RStudio. This package is available for Windows, Mac, and Linux and can be downloaded for free from http://rstudio.org.
- From the CRAN archive at https://cran.r-project.org, you can download R for Windows, Mac, and Linux.
- An in-depth "Introduction to R" training manual is available at http://cran.r-project.org/doc/manuals/R-intro.pdf
This course can be taken individually, or as one of four courses required to receive the CPDA certificate of completion. It is required that participants will have taken Introductory Statistics for Data Analytics first and Introduction to Data Mining before this course as well as Linear Algebra and Calculus for Machine Learning if necessary.
Are You Prepared For This Course?
Below is a link to a quiz you can use to assess your background and preparedness for this course. If you are not certain if you meet the pre-req's, or if it has been some time since you took coursework in linear algebra and calculus, this quiz will help. It is not graded. It's a self-assessment with instructions included by the course instructor. If you fail or struggle with the quiz, it's recommended that you complete the Linear Algebra and Calculus for Machine Learning course before taking Machine Learning.
Assessment Quiz for Machine Learning
Click Here to learn more about how this course is delivered 100% online!
Expected Time Commitment to Complete this Course
Each course is equivalent to a one semester credit hour class. Therefore each class consists of approximately 40 hours of class time that includes 12-13 hours of recorded faculty lectures and 23-24 hours of additional course work. Each course is seven weeks in length, so each week there is 5.7 hours of combined class time (40 hrs / 7 weeks). The average student should allow a 2:1 study-to-class-time ratio to complete the course. This means you should plan to study two hours for each one hour of class time. This equates to 11-12 hours each week to complete all course work. (5.7 hrs X 2 = 11-12 hrs). Based on a person's own personal strengths and experience, you should increase or decrease the ratio.
Cancellations and Refunds
A full refund minus a $75 administrative fee will be made if cancellation is received three weeks prior to the start of the course. No refunds within three weeks of the course start date.
Course Offering Dates
Each course offering in this program is faculty lead and therefore operates with a specific start date and end date. Students must complete each course during the specific time frame. Access to the online course and materials is removed when the course ends.