PHY2895.02 - Machine Learning and Neural Networks

Spring 2019

Instructor: Scott H. Hawley

Course Description: This course presents an overview of current machine learning techniques and applications, with particular attention to neural network models. Topics include: supervised, unsupervised and reinforcement learning approaches; classification & regression tasks; Deep Learning architectures; recommendation, natural language processing, computer vision and audio recognition applications. We will also give attention to issues of ethics and society, including bias, transparency and accountability. Suitable for students in many fields, including but not limited to: Computer Science, Business, Physics, Neuroscience, Audio Engineering, and Sociology.

Prerequisite: Pre-calculus or permission of instructor

Credit: 3 Hours. Approved for BELLCore general education science credit or Physics (PHY) major/minor credit. Does not count for Computer Science (CSC) major/minor credit.

NOTE: Most of the course materials are on Blackboard, which requires a Belmont login to access. The following are just some excerpts to illustrate this unique "general education" course where "ethics and society", "math", and "computer code" are integrated.

Syllabus: PDF, HTML

Course poster:

I. Excerpts from Syllabus:

Required Book:: Hello World: How to be Human in an Age of Algorithms by Hannah Fry, W. W. Norton & Company (2018). Hannah Fry's book is an excellent, accessible and entertaining overview of ethical issues relating to justice, politics, medicine and more, as they are impacted by algorithmic decision-making in general and machine learning in particular.
(Plug for another book: Grokking Deep Learing by Belmont alumnus, Oxford Ph.D. student and DeepMind researcher Andrew Trask. Not required.)

Course Objectives

Students will be able to discuss, present, answer questions about, write at length about, execute and/or implement concepts and ideas in the three major areas of Context, Theory, and Execution:
General Education Goals (BELLCore format)

Assessments and Grading

During this course, you will be asked to:

II. Learning Modules (adding as we go)

We'll be following a "flipped" or "hybrid" course model. The following are to be completed individually outside of class, prior to class. Class time will be spent discussing these, and adding new material as notes on the board or computer demonstrations and exercises.

ML Orientation (before Day 2)

ML Overview (before Day 3)

History of NN (before Day 4)

...more will be added as we progress through the course, e.g. from AI Ethics Resources
Sponsored by a grant given by Bridging the Two Cultures of Science and the Humanities II, a project run by Scholarship and Christianity in Oxford, the UK subsidiary of the Council for Christian Colleges and Universities, with funding by Templeton Religion Trust and The Blankemeyer Foundation.