- a course in
algorithms
- familiarity with Unix
- programming proficiency
- no fear of math
Machine Learning is the study
of computer programs that automatically improve their performance through
experience. This course covers the theory and practice of machine learning
from a variety of perspectives. We cover topics such as learning decision
trees, neural network learning, statistical learning methods, genetic algorithms,
explanation-based learning, and reinforcement learning. The course covers
theoretical concepts such as inductive bias, the PAC and Mistake-bound learning
frameworks, minimum description length principle, and Occam's Razor. Programming
assignments focus on hands-on experiments, such as neural network learning
for face recognition, and decision tree learning from databases of credit
records.

### G22.3033-005 Computational
Logic and Set Theory

The role of logic in mathematics.
Basics of set theory and the von Neumann encoding of integers. Propositional
and predicate calculus. Some decidability results for propositional calulus
and elementary set theory. Predicate calculus and the notion of model.
A quick look at SETL - a set-theoretic programming language. The Godel completeness
theorem. Undecidabilty and unsolvability results. Chaitin's theorem and
its consequences. Undecidabilty and unsolvability results. Hereditarily
finite sets, Godel's incompleteness theorem. Basic facts concerning cardinality,
large cardinals, inner models of set theory. Axioms of reflexivity. MLSS
decidabilty and the tableau method. Survey of the verifier system. Proof
primitives, some inital proofs. Basic definitions for set theory. The theory
mechanism Basic definitions for analysis. Some verifier system internals:
syntax trees, substitution.

### G22.3033-006 Application
Servers

An application server is a rich,
highly portable software that runs on a middle tier and handles all application
operations between browser- based pervasive devices and back-end databases
and business applications. Application servers provide a platform independent
programming interface for developing portable Java applications. Application
servers also facilitate the integration of legacy applications via on-the-fly
transformation of XML-formatted data, and support a wide variety of XML-enabled
client channels that include traditional web clients and a growing set of
smart pervasive devices. As emerging standards such as SOAP enable a new
generation of "web services" that allow systems to make remote procedure
calls to other systems over the Internet, application servers are setting
the stage as modern platforms for Web Service platforms initiatives, application
server appliances, Web services and wireless applications.

This course concentrates on
architecting, designing, and developing persistent software application using
application server technology. Throughout the course, students are exposed
to the evolution of application server architectures that started in the
mid 1990s, and experiment with corresponding approaches based on traditional
client- server technology, CGI frameworks, page-based extended HTML environments,
distributed object computing platforms, object management architectures,
component-based computing environments, and web services platforms. The course
conveys the necessary skills to select the proper application server architecture
based on project requirements and scale. The course also explains how to
integrate an application server into an existing Web site, as well as how
to implement an application server-based Web application from the ground
up. Students will learn how to configure and operate application servers
for production environment taking advantage of features available in mainstream
commercial frameworks such as scalability, concurrency, security, fault tolerance,
auto-deployment, communications support, development environment, and monitoring
tools.

As they design and implement
applications using application server technologies, students will learn how
to identify application patterns that lead to the most effective use of the
various services provided within application server frameworks. The design
and implementation of the persistence and legacy application integration
layers using related application server technology will be particularly emphasized.
Case studies provided as part of the course focus on how medium- to large-
size sites manage the complexities inherent in these endeavors. The case
studies will help students get a firm understanding of how application servers
work and how to best deploy complex applications in a real-world situation.
Although, the course will strive to provide a complete coverage and classification
of application server technology, attempts will be made whenever possible
to select open source technologies for experimentation purpose. As part of
the course, students will be exposed briefly to next generation reflective,
multimedia- and agent-enabled application servers with support for model
driven architectures.

### G22.3033-007 Special
Topics in Problem Solving

Prerequisites: A- or better
in Fundamental Algorithms. Comfort with some prototyping language
and with interfaces to the web.

Interesting problems usually
can't be solved in closed form or using polynomial algorithms. Ambulances
can't be scheduled based on rough hunches in a castrophe. Shipping companies
must exploit non-linear constraints involving profit margins, demand, and
volume, and still keep the boats moving. The same goes for optimal substring
search for microarray design or optimal portfolio management. The people
who program the practical solutions to these problems comprise the elite
of their technical organization. They are sought after throughout their careers,
because of their versatality, imagination, and programming talent. I call
them * omniheurists* -- solvers of all problems.

This course aims to develop
your skills as an omniheurist. We will study problems that require heuristics
and approximation algorithms. The heuristics include branch and bound, simulated
annealing, tabu searching, evolutionary algorithms, and adaptive gradient
methods. Approximation algorithms to NP-complete problems will help for subproblems.
The problems take minutes to explain but the challenge they pose will keep
you busy in a creative way for a few hours. I don't promise an easy course,
but I believe you will enjoy this class and find it useful.

This is a course for students
who can think creatively about algorithms and are willing to prototype them.
I will spend an hour teaching the prototyping language I use (for all of
my research) but you are free to use any you like.

In the fall of 2002, the course
emphasis will be on problem-solving in two areas: distributed computing and
biological computing. The distributed computing projects will involve adversarial
games in which distributed computing goals (e.g. ambulance pickup; competitive
consumption) must be achieved in the context of an adversary who can cause
failures or otherwise try to thwart you. The biological computing projects
will involve combinatorial problems having to do with verifying DNA sequences
using Crick-Watson pairing, the reconstruction of sequences using restriction
enzymes, the use of combinatorial design for experiments, and and so on.
The course will present all the necessary domain knowledge. At the end,
you will be able to face a difficult problem in an area where the domain
is not familiar to you, but still be able to contribute a computational solution.

### G22.3033-010 Topics
in Cryptography

This course will deal primarily
with public-key cryptography; specifically, identification schemes, signature
schemes, and asymmetric encryption schemes. The emphasis will be on practical
cryptographic schemes while at the same time focusing on rigorous, mathematical
security analysis. Although there are no formal pre-requisites, the course
"Introduction to Cryptography" (G22.3033-003) would be quite helpful. In
any case, a student should have knowledge of some of the rudiments of cryptography
(or be willing to read up on it); also helpful is some exposure to complexity
theory, algorithms, and probability theory.

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