CSCI3345
Machine Learning
This course provides an introduction to the field of machine learning. Specific learning paradigms to be covered include decision trees, neural networks, genetic algorithms, probabilistic models, and instance-based learning. General concepts include supervised and unsupervised adaptation, inductive bias, generalization, and fundamental tradeoffs. Applications to areas such as human-machine interaction, machine vision, bioinformatics, and computational science will be discussed.
Course overview
- Department
- Computer Science
- School
- MCAS
- Credits
- 3
Requirements fulfilled
No source-backed degree requirement is attached to this course yet.
Official evaluation summary
3.47 / 5
Data freshness
Instructors
Sections
- Section 01