Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

From the Publisher:
With this text, you gain an understanding of the fundamental concepts of algorithms, the very heart of computer science. It introduces the basic data structures and programming techniques often used in efficient algorithms. Covers use of lists, push-down stacks, queues, trees, and graphs. Later chapters go into sorting, searching and graphing algorithms, the string-matching algorithms, and the Schonhage-Strassen integer-multiplication algorithm. Provides numerous graded exercises at the end of each chapter.


0201000296B04062001

The Design and Analysis of Computer Algorithms

A digital computer is generally believed to be an efficient universal computing device; that is, it is believed able to simulate any physical computing device with an increase in computation time by at most a polynomial factor. This may not be true when quantum mechanics is taken into consideration. This paper considers factoring integers and finding discrete logarithms, two problems which are generally thought to be hard on a classical computer and which have been used as the basis of several proposed cryptosystems. Efficient randomized algorithms are given for these two problems on a hypothetical quantum computer. These algorithms take a number of steps polynomial in the input size, e.g., the number of digits of the integer to be factored.

Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer

Our growing dependence on increasingly complex computer and software systems necessitates the development of formalisms, techniques, and tools for assessing functional properties of these systems. One such technique that has emerged in the last twenty years is model checking, which systematically (and automatically) checks whether a model of a given system satisfies a desired property such as deadlock freedom, invariants, and request-response properties. This automated technique for verification and debugging has developed into a mature and widely used approach with many applications. Principles of Model Checking offers a comprehensive introduction to model checking that is not only a text suitable for classroom use but also a valuable reference for researchers and practitioners in the field. The book begins with the basic principles for modeling concurrent and communicating systems, introduces different classes of properties (including safety and liveness), presents the notion of fairness, and provides automata-based algorithms for these properties. It introduces the temporal logics LTL and CTL, compares them, and covers algorithms for verifying these logics, discussing real-time systems as well as systems subject to random phenomena. Separate chapters treat such efficiency-improving techniques as abstraction and symbolic manipulation. The book includes an extensive set of examples (most of which run through several chapters) and a complete set of basic results accompanied by detailed proofs. Each chapter concludes with a summary, bibliographic notes, and an extensive list of exercises of both practical and theoretical nature.

/pdf/principles-of-model-checking-249iimickz.pdf

Principles of Model Checking

SPIN is an efficient verification system for models of distributed software systems. It has been used to detect design errors in applications ranging from high-level descriptions of distributed algorithms to detailed code for controlling telephone exchanges. The paper gives an overview of the design and structure of the verifier, reviews its theoretical foundation, and gives an overview of significant practical applications.

/pdf/the-model-checker-spin-1bgo4jtefp.pdf

The model checker SPIN

Complete Boolean lattices.- Galois connections.- States, valuation functions and predicates.- The algebra of commands.- Communication and time.- Data refinement.- Transputer base model.- A small hard real-time programming language.- A hierarchy of views.- Compiling-correctness relations.- Translation theorems.- A functional implementation.- Conclusion.

Modular Compiler Verification: A Refinement-Algebraic Approach Advocating Stepwise Abstraction

In this paper we study languages accepted by nondeterministic $\log n$-tape automata that scan their input only once and relate their computational power to two-way $\log n$-tape automata. We show that for the one-way $\log n$-tape automata the nondeterministic model (1-NL) is computationally much more powerful than the deterministic model (1-L), that under one-way $\log n$-tape reductions there exist natural complete languages for these automata and that the complete languages cannot be sparse. Furthermore, we show that any language complete for nondeterministic one-way $\log n$-tape automata (under 1-L reductions) is also complete for the computationally more powerful nondeterministic two-way $\log n$-tape automata (NL) under two-way $\log n$-tape reductions. Therefore, for all bounds $T(n)$, $T(n) \geqq \log n$, the deterministic and nondeterministic $T(n)$-tape bounded computations collapse iff the nondeterministic one-way $\log n$-tape computations can be carried out by two-way deterministic log n-ta...

/pdf/languages-simultaneously-complete-for-one-way-and-two-way-3emrpokf7l.pdf

Languages Simultaneously Complete for One-Way and Two-Way Log-Tape Automata

Godel, von Neumann and the P=?NP Problem

ZB 2000: Formal Specification and Development in Z and B

This paper describes the state of the art for cryptographic primitives that are used for protecting the authenticity of information: cryptographic hash functions and digital signature schemes; the first class can be divided into Manipulation Detection Codes (MDCs, also known as one-way and collision resistant hash functions) and Message Authentication Codes (or MACs). The theoretical background is sketched, but most attention is paid to overview the large number of practical constructions for hash functions and to the recent developments in their cryptanalysis. It is also explained to what extent the security of these primitives can be reduced in a provable way to realistic assumptions.

Juris Hartmanis

Papers

Modular Compiler Verification: A Refinement-Algebraic Approach Advocating Stepwise Abstraction

Languages Simultaneously Complete for One-Way and Two-Way Log-Tape Automata

Godel, von Neumann and the P=?NP Problem

ZB 2000: Formal Specification and Development in Z and B

State of the Art in Applied Cryptography