DIGA675 Foundations of Machine Learning (3 cr.)

Prerequisite(s): DIGA 660  
This course introduces machine learning concepts and techniques used in data science. Machine learning models and associated computer programming languages are examined, with extensive applications of advanced Python programming. The course introduces supervised and unsupervised machine learning, learning theory, reinforcement learning and adaptive control. Machine learning applications to areas such as data mining and natural language processing are explored. Machine learning computer technology is utilized. A focus of the course is the application of machine learning to data collection and preprocessing, statistical modelling, and predictive analytics.

Upon completion of this course, students are expected to be able to do the following:

1. Explain the foundational machine learning concepts, mechanics, and best practices.
2. Demonstrate multiple machine learning techniques such as pattern recognition and statistical hypothesis testing.
3. Apply the data requirements for regressions, classification, and clustering machine learning activities.
4. Implement data cleansing, normalization, and standardization techniques.
5. Demonstrate use of advanced Python programming as applied to machine learning.
6. Evaluate various ethical considerations pertaining to data mining and analysis.