Instructor:   Dr. Kevin Knuth
Contact:   kknuth@albany.edu PH 211, 442 - 4653
Office Hours:   Wednesdays 10:30am - 12:00pm, PH 211
Teaching Assistant:   Nabin Malakar, John Muckell

Course Description:   Introduction to both the principles and practice of Bayesian and maximum entropy methods for data analysis, signal processing, and machine learning. This is a hands-on course that will introduce the use of the MATLAB computing language for software development. Students will learn to write their own Bayesian computer programs to solve problems relevant to physics, chemistry, biology, earth science, and signal processing, as well as hypothesis testing and error analysis. Optimization techniques to be covered include gradient ascent, fixed-point methods, and Markov chain Monte Carlo sampling techniques. 3 credits.

Prerequisite(s): A Mat 214 (or equivalent) and A Csi 101 or A Csi 201.

Written Homework:
Written homework assignments will be assigned approximately weekly. The solutions will be due 9 days after being assigned. All written homework assignments are expected to be completed and written in a neat and professional manner.

Programming Homework:
Programming homework will be assigned in conjunction with written homework. The due dates are listed above. Programs are expected to be written as Matlab m-file functions. In many cases, the instructor will provide the data to be analyzed, and the student is expected to turn in a computer generated solution along with a zip file containing the software. The instructor should be able to open the zip file, run the software successfully on his own machine, and obtain identical results.

Exams:
There will be two exams on Oct 18 and Nov 15, which will cover the material in the first half of the class. There will be no final exam.

Student Projects:
Final Projects are required for Graduate Students. Students can choose to work individually or in groups of two to propose, perform, and present a final project for the course. This project will be a project that uses methods taught in this course to solve a data analysis or signal processing problem. Project proposals are written proposals describing the problem and proposed solution. The proposals are due on Nov 1 st, and must be approved by the instructor. The projects must be completed by Dec 11 th when they will be presented to the class in a 10-15 minute presentation. Accompanying this will be a project report, which should follow the format of a short 4-8 page research paper including an abstract, introduction, method, results, conclusion, and references, along with the submission of a zip file containing the data and code.

Undergraduate Grading:
(students will choose by selecting whether to do a final project or an additional HW set)

Undergraduate Option I
Written HW 30%
Programming HW 30%
Exams 40%
Final Project: Optional Extra Credit

Undergraduate Option II
Written HW 20%
Programming HW 20%
Exams 30%
Final Project 30%  

Graduate Grading:
Written HW 20%
Programming HW 20%
Exams 30%
Final Project 30%