|
Course Description:
This course provides a foundation for machine learning concepts,
biological foundations, and
implementation for using machine learning concepts
as well as empowering students taking next level
machine learning courses.
Course Objectives:
-
Students will be able to demonstrate and build small unsupervised and supervised Neural Network algorithms on digital computers.
-
Students will become familiar with the history of Machine Learning, the core single layer and two-layer supervised and unsupervised algorithms in machine learning, and with the neurobiological and physical computation foundations of Machine Learning.
Course Outcomes:
After successfully completing this course, students will be able to
- Recognize core components and structures of neuro-inspired systems
- Analyze Neural Network systems for a particular application
- Design small Neural Networks using supervised and unsupervised learning
Video watching : Videos are expected to be watched before monday morning class.
Honor Code :
You are expected to uphold the honor code.
Each student should govern themselves
at least at the ethics of the Georgia Tech Honor Code.
Cheating will not be tolerated in this course,
and will be referred to the dean of students office.
All violations will be referred to the Dean of Students for investigation and penalties.
Attendance in class is strongly encouraged, and class discussions may contain useful technical or administrative information
|
