CIS 107 - Machine Learning Fundamentals

Credit(s) 3.00
Lecture Hours: 3.00
This course introduces the fundamental concepts and techniques of machine learning, focusing on supervised and unsupervised learning methods, model evaluation, and practical implementation. Students will learn linear and logistic regression, k-nearest neighbors, decision trees, clustering techniques such as k-means, and PCA for dimensionality reduction. Evaluation metrics, including accuracy, precision, recall, F1-score, and ROC-AUC, will be covered in detail. Hands-on projects will provide experience in applying these methods to real-world datasets. No

Prerequisite(s):

CIS 121

SLOs:
Upon successful completion of this course, students will be able to:

Explain the differences between supervised and unsupervised learning.
Implement linear regression, logistic reasoning, k-nearest neighbors, and decision tree models.
Apply clustering methods such as k-means and perform dimensionality reduction using PCA.
Evaluate models using accuracy, precision, recall, F1-score, and ROC-AUC metrics.
Use cross-validation for model selection and tuning.
Work with real-world datasets to complete end-to-end machine learning projects.
Present findings through visualizations and written reports.