Section: In English: Notes for the preparation of the exam on 19.01. & 22.01. 2024 | Artificial intelligence in applications. Modeling, Machine Learning and Data Classifier Performance | moodle-thab

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  • General

    • Wednesday same time (8:00-9:30 & 9:45 - 11:45) Lectrures & excercises in person, Building 20, room 211 Forum
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      Dear students,

      On Wednesday, 27.11.2024, and all following lectures we will continue with the lectures and excercises in our Artificial Intelligence courses as usual (not any online meetings).

      We meet as plant in  building 20 - room 211

      8:00 - 9:30 Uhr
      V Cognitive and object-oriented modeling - under uncertainties in knowledge and data - as aspects of a
      Rechnerraum 20-211

      and

      9:45 - 11:15 Uhr
      V Artificial intelligence in applications. Modeling, Machine Learning and Data Classifier Performance
      Rechnerraum 20-211

      See you on Wednesday, in C1/20-211 !

      Prof. Dr. Galia Weidl


    • List of topics to select for your Student's Presentation on Bayesian Networks. Selecting completely free your own topic of interest is encouraged! File
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      Plan for the students' presentations in WiSe2024/25: 

      You can select a topic from the list of available topics/materials or you can search for new materials and/or you can prepare your own example.

      The dates are tentative for the presentation in the class. 

      We can decide to shift them all by 1-2 weeks later. Everyone should have one presentation in the class.

      First, you do the  presentation (20 min) and then we model (15-20 min) together with everyone in the class the model you have presented.

      The oral exams for everyone will be probably on 21/22.01.2025, after we get an official booking from the dean office.

      Prof. Galia Weidl
      ___
      If you prefer to prepare your own application: How can this presentation be arranged?
      If you chose a topic (e.g. a small real application) where you need to prepare the (e.g. found from internet) data for learning of the probabilities of your model, so you can split the work into two:

      - one student can work on the structure of the model and collect all information necessary to build the structure ( graph of the network)
      - second student will then learn from data the probabilities for the created structure.

      In the class I will show you with one automotive example (maneuver recognition for cars) how this can be done. And then you can do it in a similar way.

      Where to find data?
      My PhD students will show you (in the comming classes, im November/December) some web-pages, where you can find data to build your application.

    • _Course 7540: Artificial intelligence in applications. Modeling, Machine Learning and Data Classifier Performance Lesson
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      Schedule every Wednesday, 9:45 - 11:15

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      General Information: each course has 2 ECTS      , according the the following structure:

      7525: Cognitive and Object-Oriented Modeling Under Uncertainties as Aspects of Artificial Intelligence in Practical Applications. (=2 ECTs)

      7540: Artificial intelligence in applications. Modeling, Machine Learning and Data Classifier Performance.(=2 ECTs)

      Your personal contribution will be organized as follows:
      • choose a topic/problem you would like to model with Bayesian Networks
      • do the correspondiing study of related publications on the topic of your choice (this will be the background for the development of your model),
      • Modelling: choose a model from the literature to present OR develop your own model, according to the modeling princiles we have learned in the course 7525 (= 2 ECTs)
      • search for open source data on your topic in order to learn the parameters of the model   (Maschine Learning as we will learn in course 7540) = 2 ECTS
      • For the final exam: 20 min  presentation of a model of your choice and answer questions (listed in moodle) - for each course

      Only for the incoming students: 

      In case you prefer to participate only in 7525 or only in 7540: 

      In each course 7525 or 7540: give 20 min presentation on Modelling and 15 min modeling uin the class together, so that everyone can reproduce the model you developed.

      For  participants in both courses, you will have in each course: 

      course 7525: 20 min presentation on Modelling and 15 min modeling together, so that everyone can reproduce the model you developed.

      course 7540: 20 min on Learning with Data for the model you have developed 

      The exam for eah course will consist of 15 min presentation on the selected subject and covers the questions which will be given in the class.
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      Note for the incoming students:
      International Office – Incoming Coordinator: Carina Blaeser
      Aschaffenburg University of Applied Sciences
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      I wish everyone a lot of success!
      Prof. Dr. Galia Weidl


    • Process on the Student Research Project: BN for Road Congestions File
    • Exam Planning for both courses with Prof. Galia Weidl in January 2025 (... Artificial Intelligence) Forum
      Please book your time slot for the exam in January 2025 here:
      https://moodle.th-ab.de/course/view.php?id=7293
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      To allow booking, you need first to subscribe to the moodle page  

      Exam Planning (Galia Weidl)

      Einschreibeschlüssel: AI_MyChoice1

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      This is the exam planning  (oral presentation)  for both courses:

      Course 7525: Cognitive and object-oriented modeling under uncertainties as aspects of artificial intelligence in practical applications
      &
      Course 7540: Artificial intelligence in applications. Modeling, Machine Learning and Data Classifier Performance

      Please select in (Prüfung) Planer one time slot  (Zeitfenster) of 15min if you are taking just one course
      or 2 time slots together 30 min (in case you have 2 x 15 min) for your oral presentation 

      Tuesday AFTERNOON (21.01.2025 in Campus C3/Room 107, 

      Exam time 14:00 - 19:00, 

      OR on 

      Wednesday (22.01.2025), 08:00 - 18:00, in Campus C1/Building 04/Room 203 

      Exam time 08:00 - 17:00

      I wish you a lot of success with your exam!

      Danke & Viele Grüße / Many thanks & Best regards,

      Galia Weidl  

      Prof. Dr. Galia Weidl


    • Mentorat (Consultation/Sprechstunden) Page
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    • The lecture & excercises on Wednesday, November 6. & 13. + December 4th 2024 Forum
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      Dear Students,

      The lecture & excercises on Wednesday, December, 4th. will be given by my PhD Students.

      If you have questions about where to get some data for your model and how to prepare the data for learning the parameters of your model, you can ask them to help you.

      Best regards,

      Galia Weidl


2024-01-19 & 2024-01-22: Relevant Questions/ Preparation for the exam on 19-22 January 2024
(In German): Prüfung - Hinweise für die Vorbereitung für die Prüfung am 19.01. & 22.01. 2024

In English: Notes for the preparation of the exam on 19.01. & 22.01. 2024

  • In English: Notes for the preparation of the exam on 19.01. & 22.01. 2024

    • In English: Note on Hypothesis (Cause) und Features (Effects) File
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      Note on Hypothesis (Cause) und Features (Effects):

      A conclusion/decision is based on the Cause – Effect Relation.

      The Hypothesis is the diagnosis (i.e., the possible cause of a problem).

      How do we find that there is a problem? – Based on the symptoms of this problem, the Problem has measurable (or observable) consequences/effects.

      Therefore, we need for the decision/conclusion some features (German: Merkmale), i.e., measurements, and/or observations, which appear as consequences/effects of the Problems (causes).

      Thus, we ask ourselves (e.g., for the Burglary-Modell):

      What did we observe/measure?

      • Was an Alarm issued/sounding?
      • Did Watson call due to the alarm?
      • Did the Radio report a new message on earthquake?

      Based on the observed/measure features we conclude/decide on the causes of a certain problem (issued alarm): Burglary or Earthquake. This means, that we meet the decision on what was the possible cause (diagnosis) of a problem, based on the evidence (observations/ measurements).

       

      The HUGIN software tool is using the developed BN model to evaluate the hypotheses based on the concrete observations (evidences) of the features.

      Therefore, for the Burglary-Modell: 

      The Hypotheses are the possible causes of the problems:  Burglary or Earthquake,

      and the features are the observed effects (i.e., consequences or symptoms) of the problem and its evidences: Alarm, Watson Call, Radio-Message.

      --------------------------------------------------------

      Note:
             * The Hypotheses are generated from the Features. In other words: from the Features are contructed the Hypothesen.

             * The hypotheses are evaluated, based on the Evidence (=measured Features) and this allows to make the decision.

             The Hypotheses are used in two different types of decision tasks:

      1. Diagnosis:

                 For Diagnostics: die decision finds what Hypothesis has the highest probability, based on the observations/measurements (evidence). An example of Diagnostics task is the angina.oobn Bayes Network.

            2. Prognosis:  

      Different Hypotheses are combined in order to build the prognosis um eine Prognosis, e.g. for the Recognition of maneuvers, based on observed features.

      In the example " Recognition of maneuvers": The decision (=Maneuver Recognition) has several states: DONTCARE, OBJCUTIN, OBJCUTOUT, LANEFOLLOW, OBJFOLLOW, EGOOCUTIN, EGOCUTOUT. 
      These states are computed based on the evidence (= observed features).
      For example, the Lateral Evidence is a Hypothesis, which is build from the features (lateral velocity and laterale distance (offset).
      The features are measured and set/entered as evidence in the Hypothesis.
      A measured value (e.g. speed) is influenced by the really measured Variable (velocity) und by the uncertainty/inacuracy of measurement/Noise. 

          The decision finds what combination of Hypotheses (with the corresponding features) - in what state - has the highest probability. The ranking of states according to their probabilities allows to set priority on different states of the decision. 

    • Examples of BN to practice for your presentation// Einige Beispiele (Bayes Netze) zum Üben File
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      To download the BN examples (click on the icon).

      I have collected here some examples of BN to help your preparation for the exam. You can select one of these examples or you can also present your own example (or another known example).

      During the exam - you can explain the relevant exam questions based on your selected BN. 

      Sucessfull Preparations for the exam! 


      • //  (In German: Einige Bayes Netzwerke zu verschiedene Entscheidungsfindung Beispiele.

        Sie können ein Bayes Netz aus den eingefügten Beispiele wählen oder ein eigenen (oder ein bekannten)  Beispiel verwenden.

      • Bei der Prüfung können Sie die Prüfungsfragen anhand eines Beispiels erläutern.

      •  Viel Erfolg bei der Vorbereitung!


      We can discuss all possible question about the exam at our last lecture+excercise  on 12 Januar.

      If needed, we can also arrange an aditional meeting. We talk on January 12th. about suitable times.


2024-01-19 & 2024-01-22: Relevant Questions/ Preparation for the exam on 19-22 January 2024
(In German): Prüfung - Hinweise für die Vorbereitung für die Prüfung am 19.01. & 22.01. 2024