Computer Science

This course offers an introduction to the design, construction, and efficient use of databases, with a focus on relational databases. Topics include: data models; database modeling using the entity/relationship model; relational model; database design; SQL query language; views; integrity; transaction processing.

This course focuses on the basic methods for processing and analyzing data with deterministic and probabilistic tools. It is a preliminary course for deep learning and convolutional neural networks. The course explains how to digitize signals and data in a computer, how to represent them in different bases, and how to use these representations efficiently for various signal processing tasks. Topics include signal quantization and sampling for bit-allocation, system and data representations including but not limited to the Fourier representation, optimality of the Fourier representation, functional maps, convolutions, compression, dimensionality reduction, principal component analysis, restoration of blurred deterministic or randomly distributed data with or without random noise via filtering. Signals and systems are analyzed in the continuous and discrete settings.

This course provides an introduction to artificial intelligence including its challenges, revolution, and achievements, and covers topics within machine learning and deep learning. Topics in machine learning include principles, supervised learning, unsupervised learning, Bayesian methods, linear regression, logistic regression, K-means, and decision trees. Topics in deep learning include foundations, architectures, and algorithms.

This course introduces students to advanced statistics, applied to the biological sciences. It introduces more advanced linear and generalized linear models, as well as approaches to model building and comparison. It also covers applications of linear models to large-scale genomic data, programming, permutation-based tests, power analysis and multivariate statistics. In addition to providing the theoretical background of the approaches covered, the course puts much emphasis on practical implementation. Lectures are accompanied by weekly practical sessions in which students will work through analyses in the statistical software R, the standard in much of biological computing. 

This course provides an introduction to virtual reality. Topics: 3D sound technology; space tracker, motion tracker: mechanical, optical, ultrasound, magnetic; head mounted display (HMD), retina display; force feedback devices; modeling (prototyping, building large models, physically based modeling, motion dynamics); global illumination algorithms (radiocity, volume rendering, scientific visualization); texture mapping and advanced animation; graphics packages: OpenGL , DirectX; and high performance graphics architectures (Pixel-Planes, Pixel Machine), SGI reality engine, PC graphics (nVidia, ATI), accelerator chips and cards).