004 Datenverarbeitung; Informatik
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One of the main problems in machine learning is to train a predictive model from training data and to make predictions on test data. Most predictive models are constructed under the assumption that the training data is governed by the exact same distribution which the model will later be exposed to. In practice, control over the data collection process is often imperfect. A typical scenario is when labels are collected by questionnaires and one does not have access to the test population. For example, parts of the test population are underrepresented in the survey, out of reach, or do not return the questionnaire. In many applications training data from the test distribution are scarce because they are difficult to obtain or very expensive. Data from auxiliary sources drawn from similar distributions are often cheaply available. This thesis centers around learning under differing training and test distributions and covers several problem settings with different assumptions on the relationship between training and test distributions-including multi-task learning and learning under covariate shift and sample selection bias. Several new models are derived that directly characterize the divergence between training and test distributions, without the intermediate step of estimating training and test distributions separately. The integral part of these models are rescaling weights that match the rescaled or resampled training distribution to the test distribution. Integrated models are studied where only one optimization problem needs to be solved for learning under differing distributions. With a two-step approximation to the integrated models almost any supervised learning algorithm can be adopted to biased training data. In case studies on spam filtering, HIV therapy screening, targeted advertising, and other applications the performance of the new models is compared to state-of-the-art reference methods.
Es wird ein Informatik-Wettbewerb für Schülerinnen und Schüler der Sekundarstufe II beschrieben, der über mehrere Wochen möglichst realitätsnah die Arbeitswelt eines Informatikers vorstellt. Im Wettbewerb erarbeiten die Schülerteams eine Android-App und organisieren ihre Entwicklung durch Projektmanagementmethoden, die sich an professionellen, agilen Prozessen orientieren. Im Beitrag werden der theoretische Hintergrund zu Wettbewerben, die organisatorischen und didaktischen Entscheidung, eine erste Evaluation sowie Reflexion und Ausblick dargestellt.
Wir stellen die Konzeption und erste Ergebnisse einer neuartigen Informatik- Lehrveranstaltung für Studierende der Geodäsie vor. Das Konzept verbindet drei didaktische Ideen: Kontextorientierung, Peer-Tutoring und Praxisbezug (Course). Die Studierenden sollen dabei in zwei Semestern wichtige Grundlagen der Informatik verstehen und anzuwenden lernen. Durch enge Verzahnung der Aufgaben mit einem für Nichtinformatiker relevanten Kontext, sowie einem sehr hohen Anteil von Selbsttätigkeit der Studierenden soll die Motivation für fachfremde Themen gesteigert werden. Die Ergebnisse zeigen, dass die Veranstaltung sehr erfolgreich war.
Die Studienanfänger der Informatik haben in Deutschland sehr unterschiedliche Grundkenntnisse in der Programmierung. Dies führt immer wieder zu Schwierigkeiten in der Ausrichtung der Einführungsveranstaltungen. An der TU München wird seit dem Wintersemester 2008/2009 nun eine neue Art von Vorkursen angeboten. In nur 2,5 Tagen erstellen die Teilnehmer ein kleines objektorientiertes Programm. Dabei arbeiten sie weitestgehend alleine, unterstützt von einem studentischen Tutor. In dieser Arbeit sollen nun das Konzept der sogenannten „Vorprojekte“ sowie erste Forschungsansätze vorgestellt werden
Ein handlungsorientiertes, didaktisches Training für Tutoren im Bachelorstudium der Informatik
(2009)
Die didaktisch-pädagogische Ausbildung studentischer Tutoren für den Einsatz im Bachelorstudium der Informatik ist Gegenstand dieser Arbeit. Um die theoretischen Inhalte aus Sozial- und Lernpsychologie handlungsorientiert und effizient zu vermitteln, wird das Training als Lehrform gewählt. Die in einer Tutorübung zentrale Methode der Gruppenarbeit wird dabei explizit und implizit vermittelt. Erste praktische Erfahrungen mit ihrer zukünftigen Rolle gewinnen die Tutoren in Rollenspielen, wobei sowohl Standardsituationen als auch fachspezifisch und pädagogisch problematische Situationen simuliert werden. Während die Vermittlung der genannten Inhalte und die Rollenspiele im Rahmen einer Blockveranstaltung vor Beginn des Semesters durchgeführt werden, finden während des Semesters Hospitationen statt, in der die Fähigkeiten der Tutoren anhand eines standardisierten Bewertungsbogens beurteilt werden.
Regardless of what is intended by government curriculum
specifications and advised by educational experts, the competencies
taught and learned in and out of classrooms can vary considerably.
In this paper, we discuss in particular how we can investigate the
perceptions that individual teachers have of competencies in ICT,
and how these and other factors may influence students’ learning. We
report case study research which identifies contradictions within the
teaching of ICT competencies as an activity system, highlighting issues
concerning the object of the curriculum, the roles of the participants and
the school cultures. In a particular case, contradictions in the learning
objectives between higher order skills and the use of application tools
have been resolved by a change in the teacher’s perceptions which
have not led to changes in other aspects of the activity system. We look
forward to further investigation of the effects of these contradictions in
other case studies and on forthcoming curriculum change.
Companies develop process models to explicitly describe their business operations. In the same time, business operations, business processes, must adhere to various types of compliance requirements. Regulations, e.g., Sarbanes Oxley Act of 2002, internal policies, best practices are just a few sources of compliance requirements. In some cases, non-adherence to compliance requirements makes the organization subject to legal punishment. In other cases, non-adherence to compliance leads to loss of competitive advantage and thus loss of market share. Unlike the classical domain-independent behavioral correctness of business processes, compliance requirements are domain-specific. Moreover, compliance requirements change over time. New requirements might appear due to change in laws and adoption of new policies. Compliance requirements are offered or enforced by different entities that have different objectives behind these requirements. Finally, compliance requirements might affect different aspects of business processes, e.g., control flow and data flow. As a result, it is infeasible to hard-code compliance checks in tools. Rather, a repeatable process of modeling compliance rules and checking them against business processes automatically is needed. This thesis provides a formal approach to support process design-time compliance checking. Using visual patterns, it is possible to model compliance requirements concerning control flow, data flow and conditional flow rules. Each pattern is mapped into a temporal logic formula. The thesis addresses the problem of consistency checking among various compliance requirements, as they might stem from divergent sources. Also, the thesis contributes to automatically check compliance requirements against process models using model checking. We show that extra domain knowledge, other than expressed in compliance rules, is needed to reach correct decisions. In case of violations, we are able to provide a useful feedback to the user. The feedback is in the form of parts of the process model whose execution causes the violation. In some cases, our approach is capable of providing automated remedy of the violation.
This paper describes the proof calculus LD for clausal propositional logic, which is a linearized form of the well-known DPLL calculus extended by clause learning. It is motivated by the demand to model how current SAT solvers built on clause learning are working, while abstracting from decision heuristics and implementation details. The calculus is proved sound and terminating. Further, it is shown that both the original DPLL calculus and the conflict-directed backtracking calculus with clause learning, as it is implemented in many current SAT solvers, are complete and proof-confluent instances of the LD calculus.
Many formal descriptions of DPLL-based SAT algorithms either do not include all essential proof techniques applied by modern SAT solvers or are bound to particular heuristics or data structures. This makes it difficult to analyze proof-theoretic properties or the search complexity of these algorithms. In this paper we try to improve this situation by developing a nondeterministic proof calculus that models the functioning of SAT algorithms based on the DPLL calculus with clause learning. This calculus is independent of implementation details yet precise enough to enable a formal analysis of realistic DPLL-based SAT algorithms.