Course Description

Machine learning is changing how we study and understand our world. This course serves as an introduction to the foundations and basic approaches of Machine Learning and their applications. Students will use software and data tools to learn how they can be used to identify complicated patterns in data and to solve real-world problems.

Additional Requirements for Graduate Students:
Graduate student grades will reflect a deeper and richer understanding of the course material by presenting a research paper to the class and by doing a group term project.

---

Athena Title

Foundations of ML

---

Prerequisite

ARTI 2550 or MATH 1113 or MATH 1113E or one MATH course 2000-level or higher

---

Semester Course Offered

Not offered on a regular basis.

---

Grading System

A - F (Traditional)

---

Student Learning Outcomes

• Students will be able to explain fundamental concepts and approaches of Machine Learning.
• Students will be able to analyze a problem, evaluating the suitability of different machine learning techniques.
• Students will be able to use data and software tools and develop machine learning models to solve a problem.
• Graduate students will be able to conduct and evaluate the performance of machine learning models and communicate findings in written reports.

---

Topical Outline

• Machine learning definition and basics
• Supervised learning including inductive learning, decision trees, neural networks, Bayesian learning, instance-based learning
• Unsupervised learning including clustering
• Machine learning applications

---

Institutional Competencies

Analytical Thinking

The ability to reason, interpret, analyze, and solve problems from a wide array of authentic contexts.

Critical Thinking

The ability to pursue and comprehensively evaluate information before accepting or establishing a conclusion, decision, or action.

---