This interdisciplinary course provides a hands-on approach to students in the topics of bioinformatics and proteomics. Lectures and labs cover sequence analysis, microarray expression analysis, Bayesian methods, control theory, scale-free networks, and biotechnology applications. Designed for those with a computational and/or engineering background, it will include current real-world examples, actual implementations, and engineering design issues. Where applicable, engineering issues from signal processing, network theory, machine learning, robotics and other domains will be expounded upon.
Fundamental methods used for exploring the information content of observations related to kinematical and dynamical models. Basic statistics and linear algebra for inverse methods including singular value decompositions, control theory, sequential estimation (Kalman filters and smoothing algorithms), adjoint/Pontryagin principle methods, model testing, etc. Second part focuses on stationary processes, including Fourier methods, z-transforms, sampling theorems, spectra including multi-taper methods, coherences, filtering, etc. Directed at the quantitative combinations of models, with realistic, i.e. sparse and noisy observations.
Fundamental methods used for exploring the information content of observations related to kinematical and dynamical models. Basic statistics and linear algebra for inverse methods including singular value decompositions, control theory, sequential estimation (Kalman filters and smoothing algorithms), adjoint/Pontryagin principle methods, model testing, etc. Second part focuses on stationary processes, including Fourier methods, z-transforms, sampling theorems, spectra including multi-taper methods, coherences, filtering, etc. Directed at the quantitative combinations of models, with realistic, i.e. sparse and noisy observations.
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