COURSE DESCRIPTION
Department and Course CS 467
Number
Course
Title
Machine Learning
Course
Coordinator
Total Credits
Contact Hours
Bethard
3
37.5 hours
Current Catalog Description
Introduction to machine learning, the design of algorithms that can make predictions about the
future based on past experience. Emphasizes practical considerations for developing efficient
and accurate machine learning models, and theoretical underpinnings of different learning
algorithms.
Textbook
A Course in Machine Learning, by Hal Daumé III, 2012
Course Outcomes
1. To introduce principles and concepts of machine learning
2. To gain practical experience applying and evaluating machine learning algorithms
Relationship between Course Outcomes and Program Outcomes: A, B, I, J
Prerequisites by Topic
Either CS 303 Algorithms and Data Structures and CS 355 Probability and Statistics,
or CS 460 Artificial Intelligence.
Required/Elective: Elective
Major Topics Covered in the Course
- Supervised learning
- Linear models
- Probabilistic modeling
- Neural networks
- Kernel methods
- Unsupervised learning
- Feature engineering
- Model evaluation
Laboratory projects (specify number of weeks on each)
- Implementation of simple supervised algorithms (2 weeks)
- Implementation of advanced supervised algorithms (2 weeks)
- Implementation of unsupervised algorithms (2 weeks)
Criterion 3 Student Outcomes
Outcome
a An ability to apply knowledge of computing and mathematics appropriate to the
discipline
X
b An ability to analyze a problem, and identify and define the computing requirements
appropriate to its solution
X
c
An ability to design, implement, and evaluate a computer-based system, process,
component, or program to meet desired needs
d An ability to function effectively on teams to accomplish a common goal
e
An understanding of professional, ethical, legal, security and social issues and
responsibilities
f
An ability to communicate effectively with a range of audiences
g An ability to analyze the local and global impact of computing on individuals,
organizations, and society
h Recognition of the need for and an ability to engage in continuing professional
development
i
An ability to use current techniques, skills, and tools necessary for computing practice
j
An ability to apply mathematical foundations, algorithmic principles, and computer
science theory in the modeling and design of computer-based systems in a way that
demonstrates comprehension of the tradeoffs involved in design choices.
k An ability to apply design and development principles in the construction of software
systems of varying complexity.
Oral and Written Communications
None
Social and Ethical Issues
None
X
X