This course introduces the fundamental concepts and algorithms of machine learning. Students will explore supervised and unsupervised learning techniques, including regression, classification, clustering, and dimensionality reduction. Practical sessions will involve implementing and evaluating machine learning models using Python and popular libraries such as scikit-learn, NumPy, and Pandas.
Prerequisite: Grade of C or higher in COS 184.
Credits: 3
Learning Outcomes
By the end of this course, students will be able to:
- Understand the theoretical concepts behind major machine learning algorithms.
- Apply machine learning techniques to solve real-world problems in classification, regression, and clustering.
- Evaluate and improve the performance of machine learning models using various metrics.
Textbook
Instructor depend
Syllabus
Offered
Fall odd years