Computer Science

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).

This course introduces artificial intelligence and neural computing as both technical subjects and as fields of intellectual activity. The course introduces basic concepts of artificial intelligence for reasoning and learning behavior; and introduces neural computing as an alternative knowledge acquisition/representation paradigm, to explain its basic principles and to describe a range of neural computing techniques and their application areas.

This course examines deep learning. It covers the motivations and principles for building deep learning systems; how deep learning relates to the broader field of artificial intelligence; problems associated with domain specific data; recognition; image generation; reinforcement learning; language translation; computer vision; natural language processing; PyTorch; Tensorflow; and numerical optimization algorithms.

This course explains the techniques behind compilers, lexers and parsers. It looks into mathematical formalisms of regular expressions, context-free grammars, and shows their applications to computer languages and illustrates low level machine languages and compiler techniques. Students learn how to use regular expressions to scrape information from the web, how to design grammars for parsing languages and how to implement a small interpreter and compiler. Students will be able to implement the central components of a small compiler. Students will also know the theory behind lexing and parsing so that they canchoose an appropriate algorithm for recognising a computer language.