CSC 2300 Introduction to Machine Learning and AI
This course provides a comprehensive introduction to the theory and practice of machine learning, focusing on fundamental algorithms and their applications. Students will develop both theoretical understanding and practical skills for building systems that learn from data.
Division: Business and Public Services
Department: Computer Science
Repeatable Credit: No
Offered Online: Yes
Prereqs: CSC 2266
Outcomes
- Develop hands-on proficiency with industry-standard machine learning frameworks and tools, primarily using Python libraries such as scikit-learn, NumPy, pandas, and matplotlib. Learn to efficiently load and preprocess datasets, split data appropriately, train models with various algorithms, make predictions, and visualize results. Understand how to leverage built-in functions for cross-validation, hyperparameter tuning, and model evaluation while knowing when and how to customize these tools for specific needs.
- Choose suitable algorithms based on problem characteristics including dataset size, feature types, computational resources, interpretability requirements, and performance goals. Understand the tradeoffs between model complexity and generalization, and know when to prefer simpler models like logistic regression versus more complex ones like neural networks.
- Bridge the gap between practical problems and machine learning solutions by identifying whether a problem requires supervised or unsupervised learning, classification or regression, or if machine learning is even appropriate.
Credit Hours: 3
Classroom Hours: 3