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

Convergence of Probability Measures. By P. Billingsley. Chichester, Sussex, Wiley, 1968. xii, 253 p. 9 1/4“. 117s.

Convergence of Probability Measures

https://sci2s.ugr.es/sites/default/files/files/Teaching/GraduatesCourses/Metaheuristicas/Bibliography/VNS.pdf

Variable neighborhood search

The highly successful architecture and protocols of today's Internet may operate poorly in environments characterized by very long delay paths and frequent network partitions. These problems are exacerbated by end nodes with limited power or memory resources. Often deployed in mobile and extreme environments lacking continuous connectivity, many such networks have their own specialized protocols, and do not utilize IP. To achieve interoperability between them, we propose a network architecture and application interface structured around optionally-reliable asynchronous message forwarding, with limited expectations of end-to-end connectivity and node resources. The architecture operates as an overlay above the transport layers of the networks it interconnects, and provides key services such as in-network data storage and retransmission, interoperable naming, authenticated forwarding and a coarse-grained class of service.

/pdf/a-delay-tolerant-network-architecture-for-challenged-4nomv5ym6e.pdf

A delay-tolerant network architecture for challenged internets

An Introduction to MultiAgent Systems.

Resource-constrained project scheduling: Notation, classification, models, and methods

Algorithmic graph theory and perfect graphs

Project networks with time windows are generalizations of the well-known CPM and MPM networks that allow for the introduction of arbitrary minimal and maximal time lags between the starting and completion times of any pair of activities. We consider the problem to schedule such networks subject to arbitrary (even time dependent) resource constraints in order to minimize an arbitrary regular performance measure (i.e. a non-decreasing function of the vector of completion times). This problem arises in many standard industrial construction or production processes and is therefore particularly suited as a background model in general purpose decision support systems. The treatment is done by a structural approach that involves a generalization of both the disjunctive graph method in job shop scheduling [1] and the order theoretic methods for precedence constrained scheduling [18,23,24]. Besides theoretical insights into the problem structure, this approach also leads to rather powerful branch-and-bound algorithms. Computational experience with this algorithm is reported.

Scheduling project networks with resource constraints and time windows

In this paper we deal with the substitution decomposition as known for Boolean functions, set systems and relations. It is shown how combinatorial optimization problems, particularly on graphs, partial orders, project networks, (in-) dependence systems and clutters, naturally lead, under weak assumptions, to this kind of decomposition via certain uniqueness results. We then generalize the common features of this type of decomposition from the special cases to a quite general algebraic level, covering infinite cases, and deduce a general Jordan-Holder theorem as well as uniqeness results for the associated composition tree in this general setting. These results are then reinterpreted for the special cases, and the computational aspects of the substitution decomposition are discussed. Throughout, applications to many problems concerning discrete structures are included, as are connections with other approaches in these fields, in particular the split decomposition.

Substitution Decomposition for Discrete Structures and Connections with Combinatorial Optimization

We present a new concept for optimization under uncertainty: recoverable robustness A solution is recovery robust if it can be recovered by limited means in all likely scenarios Specializing the general concept to linear programming we can show that recoverable robustness combines the flexibility of stochastic programming with the tractability and performances guarantee of the classical robust approach We exemplify recoverable robustness in delay resistant, periodic and aperiodic timetabling problems, and train platforming

Rolf H. Möhring

Papers

Resource-constrained project scheduling: Notation, classification, models, and methods

Algorithmic graph theory and perfect graphs

Scheduling project networks with resource constraints and time windows

Substitution Decomposition for Discrete Structures and Connections with Combinatorial Optimization

The Concept of Recoverable Robustness, Linear Programming Recovery, and Railway Applications