Michael L.'s Senior Project Blog!

Machine learning is used all over the world to efficiently and quickly sort large amounts of data into trends and patterns. During my summer internship I used machine learning, specifically the SVM algorithm (Support Vector Machine), to divide a pregnant woman’s heartbeat wavelength frequency from the baby’s kicking frequency. The use of machine learning in this instance was able to factor out the unnecessary information that could have caused confusion when analyzing the data. These algorithms also make technologies “Highly effective at data mining, particularly on a continual, ongoing basis, as would be required for an algorithm”. My internship will take me to Shanghai to work as an intern at Delta Electronics. The internship will revolve around router testing. Although explicit details are not available at this time, I do know that my work will be mainly with experimentation and error discovery. As a side project and “main goal”, I will be attempting to individually learn machine learning principles, such as SVM (Support Vector Machine) as well as Naive Bayes, and coding languages such as Java and Python to create a program that will be able to identify an object and classify it into categories.