So what is Machine learning?
The ability of a machine to improve its own performance through the use of a software that employs artificial intelligence techniques to mimic the ways by which humans seem to learn, such as repetition and experience.
Machine learning can refer to:
• the branch of artificial intelligence;
• the methods used in this field (there are a variety of different approaches).
Overall, if talking about the latter, Tom Mitchell, author of the well-known book “Machine learning”, defines ML as “improving performance in some task with experience”. However, this definition is quite a broad one, so we can quote another more specific description stating that ML deals with systems that can learn from data.
ML works with data and processes it to discover patterns that can be later used to analyse new data. ML usually relies on specific representation of data, a set of “features” that are understandable for a computer. For example, if we are talking about text it should be represented through the words it contains or some other characteristics such as length of the text, number of emotional words etc. This presentation depends on the task you are dealing with and is typically referred to as “feature extraction”.
Over the last few years we have seen a rise in Programmatic Ad Buying where media buyers bid for impressions in real-time through RTB (real-time bidding) protocol. This industry’s shift in Ad-Tech has significantly increased the demand for exploiting machine learning algorithms for automating and optimizing ad buying process with the goal of maximizing conversion rates (e.g. sign ups, buys. game installs etc) while minimizing the CPA (i.e. cost per acquisition).
My proposed application that is interesting to be built is Email filtering. Email filtering is an extremely useful and practical problem. Email has become a mainstream form of communication. We obtain useful information each day via email but there is problem of getting unwanted emails.
Many users receive numerous unwanted emails each day which is why a spam filter has been created. The spam filter needs to sort incoming mail into wanted and unwanted. This can be tricky because the filter could allow too much spam into the inbox or could label some legitimate emails as spam. Machine learning can help solve this problem. The email client can be trained to learn where to put each email.
A machine learning algorithm for email filtering will take in a set of labeled messages as the input and will output correct labels for the testing data.
[Andriyan]
[1901458980]
[Odd Semester 2016/2017]
[School of Computer Science, Bina Nusantara University]