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a0e46abbe0
54
ISDL-SaaS/WS1617.tex
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54
ISDL-SaaS/WS1617.tex
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||||
\input{../settings/settings}
|
||||
|
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\begin{document}
|
||||
|
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\klausur{ISDL-SaaS}
|
||||
{Prof. Dr. T. Weitzel)}
|
||||
{Wintersemester 16/17)}
|
||||
{90}
|
||||
{Es sind keine Hilfsmittel erlaubt}
|
||||
|
||||
\begin{enumerate}
|
||||
\item Cloud (25 Punkte)
|
||||
\begin{enumerate}
|
||||
\item Grenzen Sie bitte »System of Engaagement« ab gegenüber »System of Record« und nennen Sie bitte jeweils beispielhafte Anwendungssoftware? (5 Punkte)
|
||||
\item Welche nicht-funktionalen Qualitäten sollte eine Software-as-a-Service mitbringen? Erläutern Sie in dem Kontext den Target-Management-Ansatz. (5 Punkte)
|
||||
\item Aus welchen drei Elementen setzen sich die Betriebskosten einer Cloud-Anwendung zusammen? (5) Punkte)
|
||||
\item Angenommen, die Betriebskosten einer Cloud-Anwendung betragen pro Kunde 50.000\,€ bei einem Multi-Tenancy-System mit 10 Kunden und Fixkosten pro System von 250.000\,€. Wie hoch belaufen sich nach Ihrer Überschlagsrechnung die Kosten pro Kunde im Falle von 1000 Kunden pro System? (10 Punkte)
|
||||
\end{enumerate}
|
||||
|
||||
\item Consumerization (10 Punkte)
|
||||
\begin{enumerate}
|
||||
\item Welche neuen Benutzergruppen adressieren Anbieter von Unternehmenssoftware durch mobile Geschäftsanwendungen, etwa auf einem Smartphone oder Tablet-PC? Nennen Sie exemplarisch zwei Benutzergruppen und jeweils konkrete Anwendungsfälle für mobile Anwendungen. (5 Punkte)
|
||||
\item Welche Vorteile bietet HTML5 gegenüber der nativen Entwicklung auf mobilen Endgeräten? (5 Punkte)
|
||||
\end{enumerate}
|
||||
|
||||
\item Big Data (25 Punkte)
|
||||
\begin{enumerate}
|
||||
\item Welche Vorteile bieten Datenbanken, die auf Column Store basieren, gegenüber klassischen Row-Store-basierten Datenbanken? (5 Punkte)
|
||||
\item Wie unterscheiden sich die Einsatzbereiche von SAP HANA vs. Hadoop? Nennen Sie typische Anwendungsbereiche für beide Technologien. (6 Punkte)
|
||||
\item Bitte definieren Sie »Big Data«. (4 Punkte)
|
||||
\item Wie lässt sich Big Data etwa im Bereich des Handels bzw. eCommerce einsetzen? Beschreiben Sie einen möglichen Anwendungsfall und dessen Nutzereffekte.
|
||||
\item Welche Methoden eignen sich insbesondere für die Analyse großer Datenmengen? Bitte erläutern Sie dies anhand des Beispiels der Sentiment-Analyse im Anwendungskontext des Beschwerde-Managements (Kunden schicken Beschwerde-Emails oder bloggen in sozialen Netzwerken). (5 Punkte)
|
||||
\end{enumerate}
|
||||
|
||||
\item Plattform-Ökonomie – Neue Vermarktungsmodelle (20 Punkte)
|
||||
\begin{enumerate}
|
||||
\item Erläutern Sie den »Plattform-Effekt« (Modell von Van Alstyne \& Parker), den sich Plattformen wie Apple mit dem iPod oder iPhone zunutze gemacht haben, gegenüber anderen Anbietern bzw. weniger erfolgreichen Plattformen?
|
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\item Welche drei Anreizkategorien sollte der Plattformanbieter in diesem Zusammenhang dem Partnerunternehmen bieten, das Lösungen (z.\,B. Apps) auf der Plattform bauen soll? (5 Punkte)
|
||||
\item Was sind die wesentlichen Vorteile eines AppStore gegenüber dem persönlichen Vertrieb? (5 Punkte)
|
||||
\item Wie könnte man das Free-Prinzip von Chris Anderson auf das Geschäft mit Unternehmenssoftware übertragen? Erläutern Sie den damit verbundenen Vertriebsansatz. (5 Punkte)
|
||||
\end{enumerate}
|
||||
|
||||
\item Internet of Things (10 Punkte)
|
||||
\begin{enumerate}
|
||||
\item Erklären Sie bitte den Begriff »Internet of Things« inkl. der wesentlichen Anwendungsbereiche. (5 Punkte)
|
||||
\item Beschreiben Sie bitte den Anwendungsfall »Connected Car«? Was sind die wesentlichen Funktionalitäten, die damit verbunden sind? Welche Vorteile haben Kunden und Hersteller? (5 Punkte)
|
||||
\end{enumerate}
|
||||
|
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% \item{
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% Hier könnte dein Bild stehen:
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% %\image{1}{Capture3.PNG}{DNS-Anfrage}{DNS-Anfrage}
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% }
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\end{enumerate}
|
||||
\end{document}
|
||||
64
KInf-DigBib-B/WS1617.tex
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KInf-DigBib-B/WS1617.tex
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\input{../settings/settings}
|
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|
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\begin{document}
|
||||
|
||||
\klausur{KInf-DigBib-B Digitale Bibliotheken}
|
||||
{Prof. Dr. Christoph Schlieder}
|
||||
{Wintersemester 16/17}
|
||||
{60}
|
||||
{-}
|
||||
|
||||
\begin{enumerate}
|
||||
\item Aufgabe 1: Digitale Bibliotheken
|
||||
\begin{enumerate}
|
||||
\item Beschreiben sie drei Dienstleistungen digitaler Bibliotheken, die sie von herkömmlichen Bibliotheken unterscheiden (3P)
|
||||
\item Die von der IFLA Study Group erstellten FRBR stellt unter anderem die Konzepte "work", "expression", "manifestation" und "item" zur Beschreibung von bibliographischen Daten bereit. Erläutern sie diese vier Konzepte und ihr Verhältnis zueinander. Geben sie für jedes der vier Konzepte ein Beispiel an (8P)
|
||||
\item Berechnen die aus der folgenden Publikationsliste den H-Index des Autors (4P)
|
||||
\begin{longtable}{c|c}
|
||||
Publikation & Anzahl der Zitationen\\ \hline
|
||||
A & 1\\
|
||||
B & 4\\
|
||||
C&11\\
|
||||
D&6\\
|
||||
E&2\\
|
||||
F&2
|
||||
\end{longtable}
|
||||
\end{enumerate}
|
||||
|
||||
\item Aufgabe 2: Indexierung und Suche
|
||||
\begin{enumerate}
|
||||
\item Welche Vorteile hat die Verwendung eines komprimierten Indexes im Vergleich zu einem nicht-komprimierten Index? Nennen sie zwei Verfahren zur Indexkompression (4P)
|
||||
\item Auf welche Weise wird die Golomb-Codierung dekodiert? Dekodieren sie zunächst den Code 1010110000101000 unter der Verwendung von $b=6$. Bestimmen sie hier zunächst die Golomb-Codierung für die Werte 0-9 (8P)
|
||||
\item erklären sie am Beispiel der Abfragen $X\wedge Y$ und $Y\wedge Z$, warum bei der Verwendung von Signaturen dennoch oft in den zurückgelieferten Dokumenten gesucht werden muss (3P)
|
||||
\begin{longtable}{cc}
|
||||
Term&Signatur\\ \hline
|
||||
$X$ & 1001 1000\\
|
||||
$Y$ & 1000 1001\\
|
||||
$Z$ & 0001 1001\\
|
||||
$X\wedge Y$ & 1001 1001\\
|
||||
$Y\wedge Z$ & 1001 1001
|
||||
\end{longtable}
|
||||
\end{enumerate}
|
||||
|
||||
\item Aufgabe3: Dokumente und Empfehlungssysteme
|
||||
\begin{enumerate}
|
||||
\item In einer Online-Videothek werden Filem von Nutzern bewertet. Berechnen sie für Nutzer Alice die fehlende Bewertung für den Film $F_3$, d.h. $r(Alice, F_3)$ mit dem GroupLens-Algorithmus. Bestimmen sie zunächst die Bewertungsähnlichkeit $sim(Alice, Bob)$ und $sim(Alice, Carol)$ und interpretieren sie das Ergebnis (12P)
|
||||
\begin{longtable}{c|ccc}
|
||||
Film\textbackslash Nutzer & Alice & Bob & Carol \\\hline
|
||||
$F_1$ & 5 &1&5\\
|
||||
$F_2$&-&-&3\\
|
||||
$F_3$&??&3&4\\
|
||||
$F_4$&1&5&-
|
||||
\end{longtable}
|
||||
1 (gefällt nicht) bis 5 (gefällt gut), keine Bewertung: -
|
||||
\item Geben sie drei Arten von Erhaltungmetadaten an, die in einem Archiv (nach dem OAIS-Referenzmodel) erfasst werden sollen (3P)
|
||||
\end{enumerate}
|
||||
\item Aufgabe4: Semantische Modellierung und Social Computing
|
||||
\begin{enumerate}
|
||||
\item Wieso setzt nach dem Verständnis Chomskys und Montagues die semantische Analyse eines natürlichsprachlichen Satzes die Analyse seiner Syntax vorraus? (4P)
|
||||
\item Beschreiben sie das Erdös-Renyi-Modell und das Barbasi-Albert-Modell für die zufällige Vernetzung von Webseiten. Welches beschreibt eher die tatsächliche Vernetzung im Internet? Begründen sie ihre antwort (6P)
|
||||
\item Welche Kritik übt Menczer am Barbasi-Albert-Modell der Webseitenverlinkung? Welche zusätzlichen Informationen nutzt sein eigenes Modell? (5P)
|
||||
\end{enumerate}
|
||||
|
||||
\end{enumerate}
|
||||
\end{document}
|
||||
0
KInf-SemInf-M/WS1516_SemInf/.gitkeep
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0
KInf-SemInf-M/WS1516_SemInf/.gitkeep
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140
KInf-SemInf-M/WS1617-SemInf.tex
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140
KInf-SemInf-M/WS1617-SemInf.tex
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@ -0,0 +1,140 @@
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||||
\input{../settings/settings}
|
||||
|
||||
\begin{document}
|
||||
|
||||
\klausur{KInf-SemInf-M}
|
||||
{Prof. Dr. C. Schlieder}
|
||||
{Wintersemester 16/17}
|
||||
{90}
|
||||
{keine}
|
||||
|
||||
\begin{enumerate}
|
||||
\item Search Methods
|
||||
|
||||
\begin{enumerate}
|
||||
\item When is a search strategy optimal? When is it complete? It is possible that a search strategy is optimal but not complete? (4 Points)
|
||||
|
||||
\item Simulated Annealing is a probabilistic optimization algorithm, which randomly selects the next state from the list of possible states. What criterion is used for determining whether or not to accept that state? (4 Points)
|
||||
|
||||
\item Places of a city are connected by a public transportation network. The travelling times between the places are specified by the edges of the graph shown below. \\
|
||||
Use the A*-algorithm to compute the shortest path from A to F. Use the Manhattan distance from a node x to the goal node F as heuristic function h(x) (e.g. h(A) = 8 + 4 = 12, h(D) = 2 + 2 = 4). (7 Points)
|
||||
|
||||
\begin{figure}[h]
|
||||
\centering
|
||||
\includegraphics[width=0.4\linewidth]{ws1617-Aufgabe1c}
|
||||
\label{fig:aufgabe1c}
|
||||
\end{figure}
|
||||
|
||||
\end{enumerate}
|
||||
|
||||
\item Search Strategies for Games
|
||||
|
||||
\begin{enumerate}
|
||||
\item In a variation of the NIM game, players alternately take (delete) one or two tokens from one of the three stacks. The player that takes the last token loses the game. The current game state is shown below. The next move is MAX's, he deletes one token from a stack with two elements. Draw the MinMax tree starting from the situation below and calculate the MIN and MAX values for all subsequent game states. Can MAX win the game? (9 Points)
|
||||
|
||||
\begin{figure}[h]
|
||||
\centering
|
||||
\includegraphics[width=0.3\linewidth]{ws1617-Aufgabe2a}
|
||||
\label{fig:aufgabe2a}
|
||||
\end{figure}
|
||||
|
||||
\item Describe the optimization technique $\alpha$-$\beta$-Prunning. Calculate the $\alpha$ and $\beta$ values for the MinMax tree from problem a). (6 Points)
|
||||
\end{enumerate}
|
||||
|
||||
\item Constraint Systems
|
||||
|
||||
\begin{enumerate}
|
||||
\item Describe the concept of constraint instantiation. What is the difference between constraint propagation and constraint instantiation? (4 Points)
|
||||
|
||||
\item For four variables K, L, M and N from the domain \{2, 3, 4, 6, 7, 8, 10, 11\} the following binary and unary constraints are known: \\
|
||||
|
||||
Binary constraints: K \{>,=\} N, M \{>\} K, N \{<,=\} L, N \{>,=\} M \\
|
||||
Unary constraints: K $\in$ \{4,8,10\}, L $\in$ \{2,3,4,6\}, M $\in$ \{2,3,7,11\}, N $\in$ \{3,4,8,11\} \\
|
||||
|
||||
Draw the constraint graph for the four variables. (4 Points)
|
||||
|
||||
\item Use the arc consistency algorithm to assign the corresponding values to the variable from problem b). Write the intermediary results in separate tables. Does the algorithm find a solution? (7 Points)
|
||||
|
||||
K: 4, 8, 10 \\
|
||||
L: 2, 3, 4, 6 \\
|
||||
M: 2, 3, 7, 11 \\
|
||||
N: 3, 4, 8, 11
|
||||
\end{enumerate}
|
||||
|
||||
\item Modeling with Logic
|
||||
|
||||
\begin{enumerate}
|
||||
\item Give a definition for the notions satisfiable formula, tautological formula, and contradictory formula in predicate logic. You may assume that the notion of a model of a formula in predicate logic has already been defined. (3 Points)
|
||||
|
||||
\item Describe the GSAT-algorithm. Is the GSAT-algorithm complete? Justify your answer. (5 Points)
|
||||
|
||||
\item Compute the most general unifier (mgu) of the two terms below. Note that w, x, y and z are variables, \underline{a} and \underline{b} are constants. \\
|
||||
|
||||
P(h(y, g(y,x)), w, g(\underline{a})) \\
|
||||
P(h(f(x), g(z, \underline{b})), f(z), g(\underline{a})) \\
|
||||
|
||||
Simplify the mgu such that the substitutions are order-independent and specify the unified term. (7 Points)
|
||||
\end{enumerate}
|
||||
|
||||
\item Ontologies and Bayesian Networks
|
||||
|
||||
\begin{enumerate}
|
||||
\item Two definitions in description logics were translated in natural language. Evaluate the correctness of the translation and justify your decision by referring to the semantics of the value restriction and the existential restriction. If necessary, specify the correct translation. (6 Points) \\
|
||||
|
||||
Definition 1: \\
|
||||
SeminarParticipant = Student $\sqcap$ $\exists$ visits($\neg$ Lecture) \\
|
||||
A seminar participant is a student that visits exactly one event that is not a lecture. \\
|
||||
|
||||
Definition 2: \\
|
||||
Student = $\forall$ visits(Lecture $\sqcup$ Seminar $\sqcup$ Tutorial) \\
|
||||
A student is someone who -- if he visits events -- only visits lectures, seminars and tutorials.
|
||||
|
||||
\item Describe the concepts of causal and diagnostic reasoning. Explain the Bayes' rule. Is it used for causal or diagnostic reasoning? (5 Points)
|
||||
|
||||
\item The following table shows the complete probability distribution for two events A and B. Explain why the values in the table are not consistent. (4 Points) \\
|
||||
|
||||
\begin{tabular}{|c|c|c|}
|
||||
\hline
|
||||
A & B & P \\
|
||||
\hline
|
||||
0 & 0 & 0,45 \\
|
||||
\hline
|
||||
0 & 1 & 0,35 \\
|
||||
\hline
|
||||
1 & 0 & 0,15 \\
|
||||
\hline
|
||||
1 & 1 & 0,15 \\
|
||||
\hline
|
||||
\end{tabular}
|
||||
\end{enumerate}
|
||||
|
||||
\item Machine Learning
|
||||
|
||||
\begin{enumerate}
|
||||
\item A training set S that is used for decision tree learning contains 8 positive and 4 negative examples. Explain how the information content I(S) is determined. \\
|
||||
|
||||
Two attributes $A_1$ or $A_2$ can be used to split S:
|
||||
\begin{enumerate}
|
||||
\item Attribute $A_1$ with values 0 or 1 splits the training set S in $S_{10}$ with 2 positive and 2 negative examples and $S_{11}$ with 6 positive and 2 negative examples.
|
||||
\item Attribute $A_2$ with values 0 and 1 splits the training set S in $S_{20}$ with 2 positive and 4 negative examples and in $S_{21}$ with 6 positive examples.
|
||||
\end{enumerate}
|
||||
|
||||
Which attribute provides the bigger information gain? Specify the formulas for the information gain Gain(S, $A_1$) and Gain(S, $A_2$) and make an educated guess about the result. (9 Points)
|
||||
|
||||
\item A perceptron consists of two unput neurons $i_1$ and $i_2$ that are connected with output neuron o via the weights $w_1$ and $w_2$. Show how the weights $w_1$=1 and $w_2$=1 are changed if the training examples shown below are processed with a learning rate of $\alpha$=1. (6 Points) \\
|
||||
|
||||
\begin{tabular}{|c|c|c|c|c|}
|
||||
\hline
|
||||
Example Nr. & 1 & 2 & 3 & 4 \\
|
||||
\hline
|
||||
$i_1$ & 1 & 1 & 0 & 0 \\
|
||||
\hline
|
||||
$i_2$ & 1 & 0 & 1 & 0 \\
|
||||
\hline
|
||||
o & -2 & -1 & -1 & 0 \\
|
||||
\hline
|
||||
\end{tabular}
|
||||
\end{enumerate}
|
||||
|
||||
\end{enumerate}
|
||||
\end{document}
|
||||
0
KInf-SemInf-M/WS1617_SemInf/.gitkeep
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KInf-SemInf-M/WS1617_SemInf/.gitkeep
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KInf-SemInf-M/WS1617_SemInf/WS1617-SemInf.tex
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KInf-SemInf-M/WS1617_SemInf/WS1617-SemInf.tex
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@ -0,0 +1,140 @@
|
||||
\input{../settings/settings}
|
||||
|
||||
\begin{document}
|
||||
|
||||
\klausur{KInf-SemInf-M}
|
||||
{Prof. Dr. C. Schlieder}
|
||||
{Wintersemester 16/17}
|
||||
{90}
|
||||
{keine}
|
||||
|
||||
\begin{enumerate}
|
||||
\item Search Methods
|
||||
|
||||
\begin{enumerate}
|
||||
\item When is a search strategy optimal? When is it complete? It is possible that a search strategy is optimal but not complete? (4 Points)
|
||||
|
||||
\item Simulated Annealing is a probabilistic optimization algorithm, which randomly selects the next state from the list of possible states. What criterion is used for determining whether or not to accept that state? (4 Points)
|
||||
|
||||
\item Places of a city are connected by a public transportation network. The travelling times between the places are specified by the edges of the graph shown below. \\
|
||||
Use the A*-algorithm to compute the shortest path from A to F. Use the Manhattan distance from a node x to the goal node F as heuristic function h(x) (e.g. h(A) = 8 + 4 = 12, h(D) = 2 + 2 = 4). (7 Points)
|
||||
|
||||
\begin{figure}[h]
|
||||
\centering
|
||||
\includegraphics[width=0.4\linewidth]{Aufgabe1c}
|
||||
\label{fig:aufgabe1c}
|
||||
\end{figure}
|
||||
|
||||
\end{enumerate}
|
||||
|
||||
\item Search Strategies for Games
|
||||
|
||||
\begin{enumerate}
|
||||
\item In a variation of the NIM game, players alternately take (delete) one or two tokens from one of the three stacks. The player that takes the last token loses the game. The current game state is shown below. The next move is MAX's, he deletes one token from a stack with two elements. Draw the MinMax tree starting from the situation below and calculate the MIN and MAX values for all subsequent game states. Can MAX win the game? (9 Points)
|
||||
|
||||
\begin{figure}[h]
|
||||
\centering
|
||||
\includegraphics[width=0.3\linewidth]{Aufgabe2a}
|
||||
\label{fig:aufgabe2a}
|
||||
\end{figure}
|
||||
|
||||
\item Describe the optimization technique $\alpha$-$\beta$-Prunning. Calculate the $\alpha$ and $\beta$ values for the MinMax tree from problem a). (6 Points)
|
||||
\end{enumerate}
|
||||
|
||||
\item Constraint Systems
|
||||
|
||||
\begin{enumerate}
|
||||
\item Describe the concept of constraint instantiation. What is the difference between constraint propagation and constraint instantiation? (4 Points)
|
||||
|
||||
\item For four variables K, L, M and N from the domain \{2, 3, 4, 6, 7, 8, 10, 11\} the following binary and unary constraints are known: \\
|
||||
|
||||
Binary constraints: K \{>,=\} N, M \{>\} K, N \{<,=\} L, N \{>,=\} M \\
|
||||
Unary constraints: K $\in$ \{4,8,10\}, L $\in$ \{2,3,4,6\}, M $\in$ \{2,3,7,11\}, N $\in$ \{3,4,8,11\} \\
|
||||
|
||||
Draw the constraint graph for the four variables. (4 Points)
|
||||
|
||||
\item Use the arc consistency algorithm to assign the corresponding values to the variable from problem b). Write the intermediary results in separate tables. Does the algorithm find a solution? (7 Points)
|
||||
|
||||
K: 4, 8, 10 \\
|
||||
L: 2, 3, 4, 6 \\
|
||||
M: 2, 3, 7, 11 \\
|
||||
N: 3, 4, 8, 11
|
||||
\end{enumerate}
|
||||
|
||||
\item Modeling with Logic
|
||||
|
||||
\begin{enumerate}
|
||||
\item Give a definition for the notions satisfiable formula, tautological formula, and contradictory formula in predicate logic. You may assume that the notion of a model of a formula in predicate logic has already been defined. (3 Points)
|
||||
|
||||
\item Describe the GSAT-algorithm. Is the GSAT-algorithm complete? Justify your answer. (5 Points)
|
||||
|
||||
\item Compute the most general unifier (mgu) of the two terms below. Note that w, x, y and z are variables, \underline{a} and \underline{b} are constants. \\
|
||||
|
||||
P(h(y, g(y,x)), w, g(\underline{a})) \\
|
||||
P(h(f(x), g(z, \underline{b})), f(z), g(\underline{a})) \\
|
||||
|
||||
Simplify the mgu such that the substitutions are order-independent and specify the unified term. (7 Points)
|
||||
\end{enumerate}
|
||||
|
||||
\item Ontologies and Bayesian Networks
|
||||
|
||||
\begin{enumerate}
|
||||
\item Two definitions in description logics were translated in natural language. Evaluate the correctness of the translation and justify your decision by referring to the semantics of the value restriction and the existential restriction. If necessary, specify the correct translation. (6 Points) \\
|
||||
|
||||
Definition 1: \\
|
||||
SeminarParticipant = Student $\sqcap$ $\exists$ visits($\neg$ Lecture) \\
|
||||
A seminar participant is a student that visits exactly one event that is not a lecture. \\
|
||||
|
||||
Definition 2: \\
|
||||
Student = $\forall$ visits(Lecture $\sqcup$ Seminar $\sqcup$ Tutorial) \\
|
||||
A student is someone who -- if he visits events -- only visits lectures, seminars and tutorials.
|
||||
|
||||
\item Describe the concepts of causal and diagnostic reasoning. Explain the Bayes' rule. Is it used for causal or diagnostic reasoning? (5 Points)
|
||||
|
||||
\item The following table shows the complete probability distribution for two events A and B. Explain why the values in the table are not consistent. (4 Points) \\
|
||||
|
||||
\begin{tabular}{|c|c|c|}
|
||||
\hline
|
||||
A & B & P \\
|
||||
\hline
|
||||
0 & 0 & 0,45 \\
|
||||
\hline
|
||||
0 & 1 & 0,35 \\
|
||||
\hline
|
||||
1 & 0 & 0,15 \\
|
||||
\hline
|
||||
1 & 1 & 0,15 \\
|
||||
\hline
|
||||
\end{tabular}
|
||||
\end{enumerate}
|
||||
|
||||
\item Machine Learning
|
||||
|
||||
\begin{enumerate}
|
||||
\item A training set S that is used for decision tree learning contains 8 positive and 4 negative examples. Explain how the information content I(S) is determined. \\
|
||||
|
||||
Two attributes $A_1$ or $A_2$ can be used to split S:
|
||||
\begin{enumerate}
|
||||
\item Attribute $A_1$ with values 0 or 1 splits the training set S in $S_{10}$ with 2 positive and 2 negative examples and $S_{11}$ with 6 positive and 2 negative examples.
|
||||
\item Attribute $A_2$ with values 0 and 1 splits the training set S in $S_{20}$ with 2 positive and 4 negative examples and in $S_{21}$ with 6 positive examples.
|
||||
\end{enumerate}
|
||||
|
||||
Which attribute provides the bigger information gain? Specify the formulas for the information gain Gain(S, $A_1$) and Gain(S, $A_2$) and make an educated guess about the result. (9 Points)
|
||||
|
||||
\item A perceptron consists of two unput neurons $i_1$ and $i_2$ that are connected with output neuron o via the weights $w_1$ and $w_2$. Show how the weights $w_1$=1 and $w_2$=1 are changed if the training examples shown below are processed with a learning rate of $\alpha$=1. (6 Points) \\
|
||||
|
||||
\begin{tabular}{|c|c|c|c|c|}
|
||||
\hline
|
||||
Example Nr. & 1 & 2 & 3 & 4 \\
|
||||
\hline
|
||||
$i_1$ & 1 & 1 & 0 & 0 \\
|
||||
\hline
|
||||
$i_2$ & 1 & 0 & 1 & 0 \\
|
||||
\hline
|
||||
o & -2 & -1 & -1 & 0 \\
|
||||
\hline
|
||||
\end{tabular}
|
||||
\end{enumerate}
|
||||
|
||||
\end{enumerate}
|
||||
\end{document}
|
||||
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14
README.md
14
README.md
@ -16,7 +16,19 @@ Das tex-Layout ist in [settings/settings.tex](settings/settings.tex) definiert.
|
||||
4. Kopier [Template/SSxx Klausurvorlage.tex](/Template/SSxx Klausurvorlage.tex)
|
||||
in den Modulordner und benenne die Datei um (folge dem Naming Schema, das
|
||||
unten aufgeführt ist).
|
||||
5. Leg los!
|
||||
5. Leg los! Es ist okay, Diagramme als Bilder (Screenshots von den Fotos)
|
||||
einzubinden.
|
||||
6. Fertig? Super! Committe deine Änderungen und pushe die tex-Datei auf den
|
||||
Server.
|
||||
7. Jetzt musst du die resultierte Klausur PDF-Datei nur noch in das
|
||||
[Klausurenmodul](https://klausuren.wiai.de/) hochladen, damit deine Klausur
|
||||
an Studierende versandt werden kann. Melde dich unter
|
||||
[klausuren.wiai.de](https://klausuren.wiai.de/) mit deinen LDAP Daten an.
|
||||
Klicke auf "Verwaltung" und wähle unter "Klausuren + Links" den Punkt
|
||||
"Klausur hochladen / Link hinzufügen" aus. Hier kannst du die PDF-Datei
|
||||
hochladen. Möglicherweise musst du dafür noch das Modul oder den Dozenten
|
||||
anlegen.
|
||||
8. Dankeeeeeeeee!
|
||||
|
||||
## Bearbeitungshinweise
|
||||
- Es ist okay, Diagramme als Screenshots von den Klausurfotos einzubinden
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user