Linear Algebra and Calculus for Machine Learning
Start Date: August 20th, 2019
End Date: October 8th, 2019
This course is for students pursuing the Certification in Practice of Data Analytics but do not have the necessary background or education required for the Machine Learning course. Or if it has been some time since you completed your education and need a refresher course in linear algebra and calculus. It is completely optional and not required otherwise to receive the certification and it cannot be counted as one of the four courses required to earn the CPDA certificate of completion.
This is a course on linear algebra and calculus for understanding machine learning algorithms. Majority of the course is designed at the level of second year undergraduate curriculum in engineering, but the last module applies the knowledge to solve simple machine learning tasks. The course covers vectors, matrices, matrix operations, eigenvalues and eigenvectors, principle component analysis, linear regression, simple classification and clustering algorithms. The course ends with a project in which the students are expected to implement the ideas studied in the course to solve a machine learning problem.
You will learn to:
- Formulate a business problem as a supervised or unsupervised learning problem
- Represent data in a vector or matrix format
- Perform vector and matrix operations
- Understand calculus of functions of multiple vectors
- Apply knowledge of linear algebra and calculus for machine learning applications
- Formulate machine learning tasks as optimization routines
- Demonstrate understanding of ethical implication of machine learning
A college level algebra course that included the basic idea of vectors and calculus.
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.
- 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 is available at http://cran.r-project.org/doc/manuals/R-intro.pdf
- 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.
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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 $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.
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.