Teaching
Course Development
Service
Teaching
I offer PHYS 699,
800, and ICS 499, 699, 700 and 800 EVERY SEMESTER.
PHYS 311
Spring 2023
PHYS 311 Classical Mechanics II
Spring 2022
PHYS 485 Professional Ethics for Physicists
Spring 2021
ICS 635 Machine Learning
Fall 2020
ICS 636 Machine Learning
Fall 2019/Spring 2020: Sabbatical.
Spring 2019
ICS 435
Machine Learning
Fundamentals
ICS 636 Machine Learning
Fall 2018
ICS 235
Machine Learning
Fundamentals
ICS 635 Machine Learning
Spring 2018
ICS 435
Machine Learning
Fundamentals
ICS 635 Machine Learning
Fall 2017
ICS 101 Tools for the Information Age
ICS 435
Machine Learning
Fundamentals
Spring 2017
ICS 636 Information Theory in Machine Learning
ICS 435
Machine Learning Fundamentals
Fall 2016
ICS 635 Machine Learning
ICS 101 Tools for the Information Age
Spring 2016
ICS 101 Tools for the Information Age
ICS 141 Discrete Mathematics
Fall 2015
ICS 636 Information Theory in Machine Learning
ICS 141 Discrete Mathematics
Spring 2015
ICS 101 Tools for the Information Age
Fall 2014
ICS 101 Tools for the Information Age
Spring 2014
ICS 141 Discrete
Mathematics
ICS 101 Tools for the Information Age
Fall 2012/Spring 2013: Sabbatical.
Spring 2012
ICS 635
Machine Learning
ICS 141 Discrete Mathematics
Fall 2011
ICS 691 Applications of
Machine Learning
ICS 141 Discrete Mathematics
Spring 2011
ICS 435
Machine Learning
Fundamentals
ICS 141 Discrete Mathematics
Fall 2010
ICS 691
Advanced Topics in Robotics
ICS 141 Discrete Mathematics
Spring 2010
ICS 635
Machine Learning
ICS 141 Discrete Mathematics
Fall 2009
ICS 141
Discrete Mathematics
Spring 2009
ICS 636
Information Theory in Machine Learning
ICS 141 Discrete Mathematics
Fall 2008
ICS 635
Machine Learning
Spring 2008
ICS 491
Neuroinformatics and
Machine Learning: From synapses to algorithms. An
Introduction.
ICS 141 Discrete Mathematics
Spring 2007
ICS 635
Computational
Intelligence / Machine Learning
Fall 2006
ICS 691
Machine
Learning
Spring 2005
ICS 691
Bioinformatics,
Machine
Learning and Quantitative Biology
SYLLABI FOR: ICS
141 Discrete Mathematics, ICS 435 Machine
Learning Fundamentals, ICS
635 Machine Learning, ICS 636 Information
Theory in Machine Learning.
Course Development
I developed the ICS machine core curriculum consisting
of the following classes:
Undergraduate classes:
- Machine Learning Foundations, ICS 235: Mathematical
foundations for machine learning.
- Machine Learning Fundamentals, ICS 435:
Undergraduate level machine learning class.
Graduate Classes:
- Machine Learning, ICS 635: Graduate level machine
learning class.
- Information Theory in Machine Leraning, ICS 636:
Advanced graduate level class.
NOTE ON ETHICS IN TEACHING
MACHINE LEARNING
Service
Science Community:
Editorial Board:
Entropy
Reviewing:
- Advances in Complex Systems
- CHAOS
- Computer Vision and Pattern Recognition
- European Biophysical Journal (EBJ)
- Entropy
- IEEE Robotics and Automation Letters
- IEEE Transactions on Neural Networks and Learning
Systems IEEE Transactions on Knowledge and Data
Engineering
- Journal of Banking and Finance
- Journal of Machine Learning Research
- Nature
- Neural Computation
- Physical Review Letters (PRL)
- Physical Review E
- Physical Review X
- Proceedings of the National Academy of Sciences (PNAS)
- Transactions on Pattern Analysis and Machine
Intelligence
- Transactions on Knowledge and Data Engineering
Proposal reviewing:
National Science Foundation
(NSF), Panelist
Conference Organization:
- Manoa
Mini-Symposium on
Physics of Adaptive Computation,
January 7, 2019, Organizer. (CCC sponsored)
- Thermodynamic
Computing
CCC
workshop,
Honolulu January 3-5, 2019, Co-organizer.
- Modeling Neural Activity (MONA): Statistics, Dynamical
Systems and Networks, Lihue, HI, June 26-28, 2013. Local
Chair. (NSF
sponsored)
Organizer
(2005-present):
Mānoa Seminar Series on
Physics of Information Processing
(formerly:
Manoa Seminar Series in Machine Learning and Computational
Neuroscience)