Material for DBBS Bio 5357 (Chemistry and Physics of Macromolecules) class [Fall 2013]
Bio5357 is a required course for incoming biophysics and biochemistry graduate students in the DBBS PhD program. It provides a survey of the chemistry and physics of the core biomolecules (proteins, nucleic acids, lipds) with an emphasis of structure, function, and associated techniques. The content here is password protected.
VMD introduction and guide for Bio5357 [Fall 2013]
General introduction to using VMD. Primarily aimed at static images, but has some information on trajectories too. Lasted updated August 2015.
Full Stanford Machine Learning course notes [Fall 2011]
Notes from Dr. Ng's machine learning course.
Cited by the following;
1. Tang, Wenlong, Ting Zhang, and Edward Sazonov. "The Application of Machine Learning in Monitoring Physical Activity with Shoe Sensors." Cyber-Physical Systems: Integrated Computing and Engineering Design, CRC Press (2013)
2. Jalota, Dhruv. "Big Data Analysis Using Amazon Web Services and Support Vector Machines." Masters Thesis, San Jose State University (2013).
3. Akindele Abisola Bankole, "Cloud Client Prediction Models for Cloud Resource Provisioning in a Multitier Web Application Environment" Masters Thesis, Carleton University, Canada (2013)
4. Jeremy R. Cooperstock (McGill University), Artifical Intelligence course 304-526 (2013)
5. Florin Rusu (University of California, Merced), "Scalable I/O-Bound Parallel Incremental Gradient Descent for Big Data Analytics in GLADE" (Research talk) (2013)
6. DTU Informatics, Course 02450: Introduction to machine learning and data modeling (2013)
7. Zhongmin Ma, "Android Application Install-time Permission Validation and Run-time Malicious Pattern Detection, Masters Thesis, Virginia Polytechnic Institute and State University (2013)
8. Javier Sánchez Monedero, "Challenges in ordinal classification: Artificial Neural Networks and Projection Based Methods ", Doctoral Thesis, University of Granada (2013)
9. Aliyeh Mousavi (Stanford University), "Reducing Rotorcraft Noise: Advances in Machine Learning for High Fidelity Design Optimization", NASA Ames Aeronautics Academy (2013)