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Machine Learning and Optimization

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Start Date: August 22, 2017 
End Date:   October 9, 2017


Machine Learning and Optimization is one of four non-credit courses in the Certification in Practice of Data Analytics (CPDA) program. 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 to approximately one semester credit hour. This is equates to around 40 hours of course work including 12-13 hours of instruction.  

This course can be taken individually, or as part of the four courses required to receive the CPDA certificate of completion. It is strongly recommended that participants who take the Foundations of Statistics first, followed by Data Mining. Machine Learning or Visualization Analytics and Sensemaking can follow in any order. 

Course Description

This course will develop a solid background for understanding the theory of machine learning and applying it in real world setting. The topics covered during the course include the history of machine learning, supervised and unsupervised learning methods, linear and logistic regression, classification problems, support vector machines (SVM), neural network, and deep learning. Each class will begin with a concrete real-world learning problem, which we will then formulate mathematically, and then develop an algorithm to solve the problem. The course will use python extensively for completing all the assignments. Towards the end of the class, we will also discuss other modern solution approaches for solving machine learning problems. The evaluation will consist of six programming assignments.

4 CEUs are granted upon successful completion of the course.

You Will Learn to:

  • Understand algorithms to solve unconstrained and constrained optimization problems
  • Learn basic supervised and unsupervised learning algorithms and their mathematical underpinnings
  • Evaluate which learning algorithms are useful for what kind of tasks
  • Assess the limits of machine learning
Recommended Prerequisites 

Familiarity with linear algebra (matrices, determinants, eigenvalues, and eigenvectors) and basic probability and statistics (random variables, expectation, mean and covariance, characteristic functions, central limit theorem, etc.)  If you're pursuing the CPDA certification, Foundation of Statistics must be taken first and it's recommended that students also take Data Mining before Machine Learning.

Students will be required to learn the R software package prior to starting the course (www.r-project.org).

Free training in R software that will prepare you for this course can be found online at:

https://www.datacamp.com/courses/free-introduction-to-r

 

Click Here to learn more about how this course is delivered 100% online!
 

Cancellations and Refunds

A full refund minus a $50 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.