Machine learning is a process of data analysis whose goal is to understand the structure of a large amount of data. Using multiple algorithms, machine learning builds on a computer’s ability to find trends in and authenticate information. What separates machine learning from other methods of data analysis, is the fact that it automates the process of building data models. The computers are not programmed with specific instructions and are instead built to observe patterns in data that create the models. This is especially helpful when researchers are looking at data without any knowledge of trends within the population. Using machine learning, data scientists have the ability to parse through much more data in an efficient way.
The two main methods of machine learning are supervised learning and unsupervised learning. Supervised learning uses data in which the desired outcomes are known. The data scientists that enter the information tell the machine what the correct outputs are so that it can compare new data to that set. This type of learning is very helpful for research that seeks to classify data and make predictions. For example, a system that detects credit card fraud based on inputs by the customer.
Unsupervised learning takes a slightly different approach. While the machine is still fed large amounts of data, the “right answers” are not provided for it. The machine is programmed to find trends in the data without knowing any correct outputs. This type of machine learning is used with large amounts of transactional data where researchers have little background knowledge about the customer makeup. A classic application of this is when data analysts use clustering to identify customer segments based on a series of characteristics. This, in turn, can be used to recognize spending habits of consumers and help to inform a well-designed marketing strategy. Some data scientists use a mix of supervised and unsupervised learning depending on the type of data that they can work with. This “semi-supervised” method is ideal when the cost of using correct outputs for all the data is too high. Typically with this strategy, only a portion of the data shown to the machine will provide the desired outcomes with the majority of the date being raw.
Machine learning relies on algorithms in order to make something out of the data it is given. Some of the common algorithms that are used include decision trees, clustering, and neural networks. Decision trees are typically used when the classifications are already known and the model can predict the outcome of new data. It uses the patterns from previous outcomes to make decisions on future cases. Clustering is a process used with unsupervised learning. This means that the model takes all the data and uses it to group the data based on characteristics associated with it. The researchers can set however many clusters they want until the best model is reached. Neural networks are very advanced and attempt to mimic the biology of the human brain. Based on large amounts of data, the system is able to adapt and update its algorithms as it learns more.
There are many everyday applications of machine learning. One great example is the Facebook Newsfeed. If a Facebook user likes or comments on a friend’s post, the system will show you more content that was posted by that friend on your feed. Another example of this is the Netflix recommendation service. If you have a Netflix account and you watch certain types of movies, the algorithm will display related movies along with other commonly watched movies based on the behaviors of past viewers. As I mentioned earlier, fraud detection is a very common application of machine learning that is used by all major card carriers to ensure the protection of their customers. An application of machine learning that we might see more of in the future is the idea of self-driving cars. This is the ultimate use of machine learning, where dozens of factors must be considered. Especially for a task as complex as driving, the machine learning required for autonomous vehicles to be successful is going to be very high.
Going forward, we can expect to see many innovations within machine learning. This process has just recently begun to take off, with many companies taking advantage of the vast amount of transactional data available to them. Google has declared a primary focus on machine learning for themselves as a company. For over a decade, they have been offering courses to their engineers about machine learning and this dedication has been demonstrated through advancements in their algorithm. As more data becomes available, there will be more opportunity for this field of artificial intelligence to grow. It will be interesting where the technology goes next.
Sources:
https://www.sas.com/en_us/insights/analytics/machine-learning.html
https://searchenterpriseai.techtarget.com/definition/machine-learning-ML
https://www.wired.com/2016/06/how-google-is-remaking-itself-as-a-machine-learning-first-company/

Professor Min-Seok Pang was promoted to Associate Professor with Tenure. Dr. Pang joined the department in in 2014 and has since built an outstanding record of scholarship around the role of information technology in government. He also is a highly-rated instructor in the BBA in MIS, the MBA program, and the Ph.D. program, teaching courses in the strategic management of information technology and data science.
Emily Repshas has been promoted to Assistant Director of MIS. Repshas joined the MIS Department in 2016. Her contributions to the department include managing the MIS PRO program, the PRO store, and expanding the department’s social media presence. In her new role, she will be adding marketing and communications to her responsibilities, including our undergraduate and master’s programs.
Professor Amy Lavin has been appointed a Dean’s Teaching Fellow for 2018. Professor Lavin has been an innovator in the classroom. She is the Academic Director of the MS in Digital Innovation in Marketing (MS-DIM). In 2017, Professor Lavin was named the MS-DIM Faculty Member of the Program, an award given based on student feedback. She has presented at conferences such as the Americas Conference on Information Systems and the Higher Education Social Media Strategies Summit.
Professor David Schuff was named the Fox School of Business Executive Doctorate in Business Administration 2018 Faculty of the Year. The award recognizes his “significant contribution to the academic and intellectual growth of Executive DBA students,” according to Academic Director and Professor of Marketing and Supply Chain Management Susan Mudambi.
Emily Schucker ’17 thought she’d major in biology when she began studying at Temple University. Frank Tkachenko ’18 planned to reinvent himself. Both found their paths thanks to the Fox School of Business.

From streamlining the dining experience to changing the way families care for ailing loved ones, the Spring MIS Capstone Showcase demonstrated the transformative impact of technology. On April 24th, 2018, nine teams of seniors from three classes, as selected by their professors, presented their final projects. These projects were the culmination of a semester (and four years) of hard work, which involved everything from creating a business plan to designing a functional prototype.
After years of hard work and study, MIS seniors always have a lot to show off at the Fox School of Business Management Information Systems Capstone Project Showcase. The most recent Showcase, held at the end of April, was no exception: The 17 teams of seniors that gathered to present their final projects unveiled everything from a medical app designed to help doctors track elderly patients’ health regimens to a personalized news aggregation service to an improved search function on Temple’s course database.
Receiving notable recognition for a research paper is not what senior Eric Koeck originally set out to do.
The Association for Information Systems honored Atish Banerjea, Chief Information Officer, Facebook, and Fox School ’91 alum, with the inaugural Association for Information Systems Leadership Excellence Award on December 12, 2016 at the 37th Annual International Conference on Information Systems (ICIS). Banerjea also delivered the keynote address at the conference while accepting the award.
Ciara Murphy doesn’t give up easily — especially when it comes to helping others.