C S 270
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Introduction to Machine Learning
Computer ScienceCollege of Computational, Mathematical, & Physical Sciences
Course Description
Understand the fundamental models of machine learning, such as neural networks, decision trees, data mining, clustering, Bayesian learning, ensembles, reinforcement learning, and deep learning. Work with data and machine learning tools in real world applications.
When Taught
Fall and Winter
Fixed/Max
3
Fixed
3
Fixed
0
Title
Machine Learning Paradigms
Learning Outcome
Identify the different types of machine learning (supervised, unsupervised, reinforcement) and the various types of problems they solve. This broad survey is intellectually enlarging, as it expands the mind's ability to categorize and approach complex, real-world phenomena through a computational lens.
Title
Model Implementation
Learning Outcome
Implement and apply several different machine learning models to a variety of datasets. The technical rigor required to move from theory to a working model is character building, fostering the work ethic and precision necessary to produce reliable, high-quality results.
Title
Performance Evaluation
Learning Outcome
Select and use appropriate evaluation metrics to assess the performance of a machine learning model. This practice of objective self-critique is spiritually strengthening, as it teaches students to value truth and accuracy over preconceived notions or desired outcomes.
Title
Feature Engineering
Learning Outcome
Apply basic feature engineering and data preprocessing techniques to prepare data for machine learning models. This careful preparation of information is a form of stewardship, encouraging students to handle data with the integrity and respect required for honest analysis.
Title
Algorithm Logic
Learning Outcome
Explain the underlying mathematical and logical principles of various machine learning algorithms. Developing a deep understanding of these foundational "laws" of learning provides a bedrock for lifelong learning, allowing students to stay grounded as new and more complex models emerge.
Title
Impact and Ethics
Learning Outcome
Discuss the ethical and societal implications of machine learning, including bias, fairness, and transparency. Grappling with these human-centric challenges is intellectually enlarging and essential for developing the character needed to use advanced technology for the benefit of all.
