Revised 03/2025

CSC 295 - Computer Science Topics in: Machine Learning Advanced Python (3 CR.)

Course Description

This course introduces machine learning concepts, and techniques for solving related problems using advanced Python. Students will learn fundamental techniques such as supervised and unsupervised learning, model evaluation, and data preprocessing. The course will cover key Python libraries, including Scikit-Learn, Pandas, and Matplotlib, to implement and analyze machine learning models. Through hands-on projects, students will gain practical experience in applying machine learning to real-world problems. No prior machine learning experience is required, but familiarity with Python programming is recommended. Lecture 3 hours. Total 3 hours per week

General Course Purpose

CSC 295 is intended as an elective course for the AS in Computer Science, and as requirements for Certificate in Computer Science program.

Course Prerequisites/Corequisites

CSC 221 is recommended.

Course Objectives

Upon completing the course, the student will be able to:

  • Define key machine learning concepts, including supervised and unsupervised learning, classification, regression, and clustering.
  • Use Python and libraries such as Scikit-Learn to build and evaluate at least three different machine learning models (e.g., linear regression, decision trees, k-means clustering) by the end of the course.
  • Apply data preprocessing techniques, including handling missing values, feature scaling, and encoding categorical variables, to improve model performance.
  • Assess the accuracy, precision, recall, and other performance metrics of machine learning models, demonstrating improvement through iterative tuning in at least two project-based assessments.
  • Explain advanced topics related to artificial neural networks.
  • Design a project that applies machine learning techniques to a real-world dataset, demonstrating the ability to clean data, select an appropriate model, and interpret results within a given deadline.
  • Present insights from machine learning models using visualizations and summaries, effectively explaining model choices and outcomes in a written or verbal format as part of a final project.

Major Topics to Be Included

  • Introduction to Machine Learning
  • Classification models
  • Regression models
  • Model evaluation
  • Model validation
  • Model improvement
  • Support vector machines
  • Decision trees applications
  • Artificial Neural Networks