Methods in Quantitative Biology

Course Directors: David Fenyo & Kasthuri Kannan, New York University (NYU)
Kasthuri's Coordinates: TRB 737, 227, E.30th St. New York
E-mail at kasthuri.kannan [at] nyumc.org (no personal emails please!)
Information Lectures

Lecture Schedule

Sept. 25, Sept. 27, Oct. 23 and Nov. 6 at 5 pm
Location: Alexandria West, Room 508

Course Information

Official Course Name: Methods in Quantitative Biology (BMSC-GA 4449)/Methodological Foundations of Biomedical Informatics (BMIN-GA 1001)

Official Course Page: Click here

Learning Objectives

The student will learn and understand the most commonly used methodologies in the field of biomedical informatics.

Course Overview

This course provides an overview of foundational knowledge and essential methods relevant for all areas of biomedical informatics. Students will explore recurring themes and application domains most frequently used in the field. The course will be technical and rigorous, and it will include a number of computer science topics. The course content has been selected by the curriculum committee, and the topics will change over time. The majority of the coursework will be programming assignments and readings.

Programming Requirement

Learning the following programming languages during the duration of the course is required:

Python - Learning Python 5, Think Python, Python for Data Analysis, SciPy, How to Think Like a Computer Scientist: Learning with Python, Learn Python the Hard Way, Python Cookbook
R - Learning R, R for Data Science, R in Action, Tutorials
JavaScript, JQuery, D3 - Tutorials
Databases - Learning SQL, MySQL Reference Manual, MongoDB the Definitive Guide, PostgreSQL: Up and Running

Reading

The following course material are recommended reading for the four lectures below:

Algorithms
Introduction to Algorithms [CLRS], (3rd ed.) (This is the gospel for algorithms course in several computer science departments).
Topics: Foundations (Chapters 1-4), Elementary Data Structures (Chapters 10-11), Elementary Graph Algorithms (Chapters 22-23) and NP-completeness (Chapter 34).
Linear Algebra
Strang, Gilbert, Linear Algebra and Its Applications [SG], (4th ed.)
Topics: Chapters 1-5
Philip N. Klein, Coding the Matrix: Linear Algebra through Applications to Computer Science [PK], (1st ed.)
Topics: Chapters 3-6,9,11,12
Optimization
Jorge Nocedal & Stephen Wright, Numerical Optimization (2st ed.)
Topics: Chapters 1-5,8
Signal Processing II - Image Processing
Rafael C. Gonzalez, Richard E. Woods, Digital Image Processing (3rd Edition)
Per Christian Hansen, James G. Nagy (Author), Dianne P. O'Leary, Deblurring Images: Matrices, Spectra, and Filtering (1st Edition)

Project presentation

You should pick and choose the recommended topics (in bold) below and present:

Algorithms -
Probabilistic Analysis and Randomized Algorithms, Heapsort, Quicksort, Sorting in Linear Time, Dynamic Programming, Greedy Algorithms, Single-Source Shortest Paths, All-Pairs Shortest Paths, Linear Programming, Polynomials and the FFT, String Matching, Approximation Algorithms. The CLRS book suggested above has these topics in depth.
Linear Algebra -
Image/signal representations and transformations, Singular value decompositions (SVD), Least squares approximation, Application of SVD in least squares, Pagerank, Linear programming and applications, Single source shortest path, Network flow, Traveling salesman, Deconvolution in signal/image processing, Blind deconvolutions, Principle component analysis, Linear discriminant analysis, Linear algebra applications to machine learning.

Lectures

Introduction to Algorithms, Sept. 25 Link Presentation
Introduction to Linear Algebra, Sept. 27 Link Presentation
Introduction to Optimization, Oct. 23 Link Presentation
Signal Processing II: Introduction to Image Processing, Nov. 06 Link Presentation