Last Reviewed/Updated: December 6, 2019
Two of the most common and important components in data science is machine learning and artificial intelligence. While these terms will be seen by each other frequently, they are completely different in terms of what they accomplish. Artificial intelligence is the actions taken by a computer, which are instructions given to them by a human that can already do said action. Machine learning is the ability for computers to learn on its own without the need for inputting commands.
Machine Learning and Artificial Intelligence Degrees (AI)
There are needs for both of these processes as we continue to depend more on computers to accomplish tasks and for our enjoyment. With so much information coming in from various human inputs, there is plenty of unstructured data to sort through and people are not able to keep up with this.
Implementing ways for computer systems to interpret this information and detect ambiguity will improve the services they offer. This makes ML and AI an incredibly important subject within data science, engineering, and management information systems.
Featured Online Data Science Programs
Johns Hopkins AAP
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George Mason University
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Northern Illinois University
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University of California Berkeley
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One of the growing opportunities within data science is pursuing higher education online. This is a convenient way to gain experience on programming skills, machine learning, and artificial intelligence while still having a full-time job.
Online programs will have either a synchronous or asynchronous format, the former being live classes in a virtual setting that usually require some form of interaction with a webcam or microphone. An asynchronous setting will have on-demand lectures and class materials that can be viewed at any time and is mostly self-paced.
Typically, there will be requirements to submit assignments or complete quizzes and exams in order to prove achievement throughout the course.
Masters in Machine Learning and AI
Especially when pursuing a Master's degree, many programs are geared toward part-time students that take one or two courses each term. If full-time programs are still too time-consuming, there are alternatives in graduate certificates or vendor certifications. Graduate certificates can be truncated forms of data science programs, or they will take portions of the core curriculum and various electives. Vendor certifications through companies like Oracle, Microsoft, and Amazon, will show competence with using a specific tool set.
An example of a vendor-based certification is through the cloud service, Amazon Web Services, on Machine Learning. They provide educational material and digital training for a specific career path, such as a program developer, business decision maker, and data scientist. Some of the specific tools featured are the AWS DeepRacer, a vehicle that is controlled autonomously around the reinforcement learning model, and Amazon SageMaker, the software used to create machine learning capabilities. Before taking the certification exam, which is $300 per attempt, Amazon recommends having at least a few years of experience in development with AWS services, and there is a cheaper practice exam to get comfortable with the questions.
Scholarships and Assistantships
High academic achievement can be awarded with scholarships and assistantships within these higher education opportunities. Other opportunities are given toward specific locations or minority groups in order to boost diversity along the university student body. For example, the VIP Women in Technology scholarship is available for women that are enrolled in a two or four-year university in the United States with career goals in data science. Candidates must submit an essay explaining why they should be considered for the $2,500 award, and must have a background in community service and a 3.0 cumulative GPA.
Many programs themselves will offer an internal scholarship for those enrolling into specific programs. For example, Nova Southeastern University provides the Edward Lieblein Memorial Fund scholarship for those enrolled full-time in the College of Engineering and Computing. This is reserved for those that are currently working within the Federal Government, and requirements include letters of recommendation from the employer, a school faculty member, and a cumulative 3.0 GPA.
Generally, the majority of scholarships available will be sponsored by an organization or individual. Experiences by these parties will be related to the requirements needed to be met for the award given out. Common requirements include sending an essay detailing why they are the best candidate, achieving high academic merit, and demonstrating financial needs.
Assistantships and fellowships provide an opportunity for successful students to become a teaching assistant or to complete a research project at the university. Essentially, they will help faculty and receive a stipend, along with the potential for lowering their tuition. Becoming an assistant as an undergraduate provides work experience and boosts networking opportunities, not to mention the potential for getting an academic job.
The Energy Industry Graduate Fellowship at Rice University offers $7,500 for students that are enrolled into a program that deals with higher-level computer science and engineering, and information technology. Students will need to work with one of the sponsors that fund the fellowship (such as BP or ExxonMobil) and this cannot be completed in the final year of the program.
Machine learning engineers are the developers of algorithms within a computer system that gives it the ability to learn without specific commands. When looking at ads on websites that are similar to other search results queried in the past, or seeing recommendations for movies and television shows based on watch history within a video streaming service, these are basic examples of the algorithm created by an engineer. Their goals are for making services easier and keeping customer retention, but with additional perspective on privacy protection and ethical standards. According to Salary.com, the average salary for an MLE position is $116,321 across the united states.
Research analysts can also fall under the umbrella of machine learning and artificial intelligence. One of the many qualifications for the job, especially with marketing, operations, and financial research, is statistical modeling techniques. They will also need to keep up to date on innovations within these algorithms to help optimize company processes. Some examples of research analysts include developing AI and ML within healthcare information systems and creating better policy within an educational system.
Companies that offer the most job opportunities that involve machine learning and artificial intelligence include Amazon Web Services, Microsoft, Apple, Facebook, Cisco, and JP Morgan & Chase. At Amazon and Apple, there are opportunities to continue optimizing their voice-enabled assistants, Alexa and Siri, respectively. Facebook is also seeing a transition in their algorithms, focusing recommended posts on what friends and family members are creating and interacting with over news sources. There is plenty of ongoing changes and improvements in this sector to find job opportunities.
Program Areas within Machine Learning and Artificial Intelligence
When looking for higher education within machine learning and artificial intelligence, there are areas of emphasis and elective courses within computer science and data science degrees. Since these topics are more advanced that standard analytics, they are generally found in graduate programs. As an example, at Columbia University, they offer a Master of Computer Science with a track in Machine Learning. This requires 30 credit hours to complete with a time limit of five years from the date of entry. The curriculum is fully available online and there is an elective course on artificial intelligence.
Machine Learning Courses for the MS in Data Science at Johns Hopkins
At the Whiting School of Engineering at Johns Hopkins, the Master of Science in Data Science can also be obtained online with the majority of courses available (also can be pursued on-campus or a combination of the two). There is a required course that serves as an introduction to machine learning, and an elective that gives more of an advanced look on topics like inductive bias, dimensionality, and the latest innovations. 10 total courses are needed to complete the program, and there is also a five-year limit.
Artificial Intelligence Track for the Masters in Data Science at Northwestern
Northwestern University’s School of Professional Studies offers a Master’s degree in Data Science and is another option that is available fully online. There are a number of specializations available to customize the degree, including an Artificial Intelligence track that has two courses: Natural Language Processing, and Artificial Intelligence and Deep Learning. As with other programs, both AI and machine learning will be emphasized, and students will learn how to create these deep learning methods for computers.
Deep Learning, MIS, Engineering
Additionally, programs that are in management information systems and engineering will also feature deep learning courses. These topics focus more on development and creating the foundation for analysts to work with. However, it is imperative to know analytical skills in order to create and evaluate AI and computer abilities, and generating algorithms that learn on their own. Boston University has an online Master of Science in Computer Information Systems. The 40-credit hour curriculum can be completed within two years, and there are various concentrations, such as Computer Networks, Health Informatics, Security, and Web Application Development.
Requirements to gain entry into a Master or PhD program that is tailored for machine learning and artificial intelligence will be what colleges require for their general data science and computer science degrees. In some cases, this material will be part of the core curriculum, but frequently, there will be electives that involve these categories, along with natural language processing and deep learning.
Typically for a Master’s degree in data science, students need to have prior coursework in statistics, calculus, linear algebra, and programming. Other requirements typically include letters of recommendation, a personal statement that details experience and goals, and GRE test scores. Sometimes, the latter is either optional or can be waived with at least a 3.0 cumulative GPA or a significant amount of work experience. Online programs will vary greatly in admission periods. Some will only accept students in the fall or spring term, while others that break things up into an eight-week course structure can allow students into the program between four to six times a year.
Requirements for graduate certifications at universities will typically be the same as admission needs to get into the Master’s program. There are specialized certifications from universities that are geared toward machine learning and artificial intelligence. Stanford’s Center for Professional Development has an online program in Artificial Intelligence with a required course on the foundation and skills of AI a number of electives with topics on robotics, reinforcement learning, and neural networks. The program is geared toward software engineers with prior coursework in probability, linear algebra, calculus, and programming.
Many data science and computer science degrees will offer core courses or electives that exclusively focus on artificial intelligence, machine learning, and deep learning. All of these courses will be required in order to pursue a career in these categories, like a systems manager, software developer, or machine learning engineer. Here are some other courses that can be found within the elective choices:
- Statistical Modeling and Algorithms
- Natural Language Processing
- Text Representation
- Reinforcement Learning
- Human-Computer Interaction
Courses in natural language processing focus on how people interact with computers and how the machine understands this. We have seen the evolution of computers and how we use them through a traditional desktop format that eventually connected online, swiping on a smartphone to order goods for delivery, and voice-activated devices that will talk back and answer queries such as current weather conditions and what times a show is playing at a local theater.
As there continues to be more unstructured data that is created every day, more than typical humans can keep up with, it is important to develop algorithms that computers can keep up with. One of the biggest challenges in this process is determining context and resolving ambiguity in language. While humans have the ability to detect is, there is an emphasis in reinforcement learning to guide these processes in a better direction.