Fall 2011 Elective Courses

Course Descriptions
Fall 2011 Course List

CSCI-UA.0436-001 8294 Computer Architecture

Prerequisites: CSCI-UA.0201 and MATH-UA.0120. Offered every fall. 4 points.
A first course in the structure and design of computer systems. Basic logic modules: arithmetic circuits. Control unit design of computers and structure of a simple processor; speed-up techniques. Storage techniques and structure of memory hierarchies; error detection and correction. Input-output structures, busses, programmed data transfer, interrupts, DMA, Microprocessors. Discussion of various computer architectures; stack, pipeline, and parallel machines; multiple functional units.

CSCI-UA.0453-001 8295 Theory Of Computation

Prerequisite: CSCI-UA.0310. Offered every fall. 4 points.
This course takes a mathematical approach in studying topics in computer science, such as: regular languages and some of their representations (deterministic finite automata, non-deterministic finite automata, regular expressions); proof of non-regularity. Context free languages and pushdown automata; proofs that languages are not context free. Elements of computability theory. Brief introduction to NP-completeness.

CSCI-UA.0470-001 8296 Object Oriented Programming

Prerequisite: CSCI-UA.0201. Offered every fall. 4 points.
Object-oriented programming has emerged as a significant software development methodology. This course introduces the important concepts of object-oriented design and languages, including code reuse, data abstraction, inheritance, and dynamic overloading. It covers in depth those features of Java and C++ that support object-oriented programming and gives an overview of other object-oriented languages of interest. Significant programming assignments, stressing object-oriented design.

CSCI-UA.0480-001 8297 Spec Topics In Comp Science: Computing with Large Data Sets

Prerequisite: CSCI-UA.0201
Enormous collections of data in multiple fields of science and engineering are being gathered and need to be analyzed. For example, the Sloan Digital Sky Survey will represent more than 200 million objects, each with 100 dimensions, and other activities in physics, biology, astronomy, and medicine will soon gather ever-larger sets of data. Biology, and more specifically the field of systems biology, have seen massive improvements in the technologies we use to sequence genomes and measure the levels of gene expression (or activity) in cells under different conditions. These large biology data sets have have features in common with large data sets arising in other fields and illustrate the general need for tools for analysis, manipulation and statistical analysis of large data sets. This course will discuss some of the associated unprecedented computational challenges, focusing on these very large data sets arising in computational biology. The course is intended to addre ss some of the needed general principles by using a high-level language, the R statistical programming language, to analyze large genomic data sets. We will focus on four main data-sets in this class that come from current genomics and systems-biology studies; the needed biology and statistics will be taught throughout the course.

CSCI-UA.0480-002 8298 Spec Topics In Comp Science: Introduction to Machine Learning

Prerequisite: CSCI-UA.0201
This course will cover a wide variety of topics in machine learning, pattern recognition, statistical modeling, and neural computation. Machine Learning and Pattern Recognition methods are at the core of many recent advances in "intelligent computing". Current applications include machine perception (vision, audition), control (process control, robotics), data mining, time-series prediction (e.g. in finance), natural language processing, web search and text mining, and text classification, bio-informatics, modeling of biological processes, and many other areas.

Students will implement a number of machine learning algorithms and will test them on real datasets for tasks such as handwriting recognition, image classification, face recognition, spam filtering, speech recognition, simulated robot control, etc.

The topics covered in the class will include: introduction to learning and generalization; linear classifiers, Perceptron, logistic regression; energy-based models and loss functions; linearly parameterized models; gradient-based learning and gradient back-propagation; non-linear models, neural nets, RBF nets, mixtures of experts, convolutional networks; Boosting; Support Vector Machines, Hidden Markov Models, Graph Transformer Networks; Unsupervised learning, clustering, dimensionality reduction; Introduction to graphical models; applications to vision, speech, text and language processing, and forecasting.