This course serves as an introduction to the fundamental principles and techniques of machine learning, a dynamic field at the intersection of computer science and statistics. Students will explore the foundational concepts underlying machine learning algorithms, gaining hands-on experience with implementing and applying these techniques to real-world problems. The course will cover a range of topics, including supervised and unsupervised learning, regression, classification, clustering, and evaluation metrics
Outcomes
- Prepare data for machine learning tasks.
- Implement and evaluate models using popular algorithms like linear regression, decision trees, and support vector machines.
- Utilize programming languages such as Python and relevant libraries (e.g., scikit-learn) for implementing machine learning algorithms.
- Apply machine learning techniques to real-world scenarios and case studies.
- Discuss ethical considerations and potential biases in machine learning applications.
- Engage in collaborative projects and discussions to enhance critical thinking skills.
- Develop the ability to communicate machine learning concepts and results effectively.
- Create reports and presentations that convey insights and findings from machine learning projects.
Prerequisites
none
Crosslisted Courses
N/A none Fall, Spring
Campus
Central
Area of Study
Career Education
HS/Tech HS