Teaching
Course Development
Service

Teaching


PHYS 699, 700 and 800, and ICS 499 and 699 are offered EVERY SEMESTER.



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:

Organizer (2005-present):

Mānoa Seminar Series on Physics of Information Processing

(formerly: Manoa Seminar Series in Machine Learning and Computational Neuroscience)

University of Hawai`i at Manoa Service

ICS Department Service

(Multiple years):