Fall 2011 Elective Courses
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
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
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.
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