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Applications of Machine Learning Techniques

This course covers a broad range and overview of machine learning techniques, including supervised, unsupervised, and reinforcement learning. Students will learn about key algorithms such as linear regression, decision trees, support vector machines, k-means clustering, and neural networks. The course emphasizes practical applications, guiding students through the process of implementing these techniques using popular programming languages and frameworks. By the end of the course, students will be proficient in selecting and applying appropriate machine learning techniques to solve complex problems, including crafting effective prompts and evaluating their performance, and understanding

  • Credits: 3 credits
  • Course ID: AIN 720

What You'll Learn

Explore the end-to-end modeling process and the different types of model algorithms along with the strengths, weaknesses, assumptions, and use cases.
Develop and deploy best practices in model creation, interpretation, validation, and selection and maintenance of models for different, real-world datasets to apply machine learning to various industries.
Evaluate model performance, including metrics such as accuracy, precision, recall, F1 score, and ROC-AUC.
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Course FAQs

What is asynchronous?
Asynchronous learning means that the instructor and the students in the course all engage with the course content at different times (and from different locations). The instructor provides students with a sequence of units which the students move through as their schedules permit.
What is the course structure?
Courses are offered fully online and are asynchronous. There is an optional, live interactive session offered weekly so students can engage with one another and with the faculty member. 

How long is each course?
Our mini-sessions (or terms) are 7.5 weeks long, and courses are offered in each mini. Each semester consists of 2 mini-sessions.
How many courses can I take in a mini-term?
We have structured our courses so students can take one course at a time; this is especially important for students balancing full-time employment. We do permit students to take a maximum of two courses in 1 mini-term (i.e. 6 credits over a 7.5 week period). While taking more than two courses in one semester may seem appealing, these online accelerated courses can be difficult to manage, especially while working full-time. In addition, taking more than one course in a mini session could cause conflicts in your weekly live sessions (i.e. they could be offered on the same day and time). 

What is a part-time course load vs. a full-time course load?
Part-time in SPS is defined as 3 credits (1 course) per semester, and full-time is defined as 6 or more credits (2 or more courses) per semester. Every course at SPS is 3 credits.
How fast can I earn a master's degree?
You can finish an SPS program in as little as 16 months if you take a course every mini semester*. If you need more time, that's okay too! We offer flexibility so you can go your own pace.

*This is for 30 credit hour programs only.