Section: 2024-01-19 & 2024-01-22: Relevant Questions/ Preparation for the exam on 19-22 January 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

      ---------------------------------------------------------------------------------------
      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
      Students must
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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


Questions to cover in your presentation: Cognitive Modeling & Learning of BN models for decision making
In English: Notes for the preparation of the exam on 19.01. & 22.01. 2024

2024-01-19 & 2024-01-22: Relevant Questions/ Preparation for the exam on 19-22 January 2024

  • 2024-01-19 & 2024-01-22: Relevant Questions/ Preparation for the exam on 19-22 January 2024

    ------------------------------------------ Relevant Exam Questions in English  ------------------------------------------------------------------------------------------------------

    - Cognitive Hypotheses  of your own BN application or an example of your choice (can be also from existing application you have selected to present)
    - Cognitive Hypotheses for Maneuver Recognition (as discussed in the lectures)

    We discuss what questions are relevant for the exam (oral examination 20 minutes for each course 7525 & 7540).

    The students can:

    • design (create/learn) a decision-making model (Bayesian network) from a domain (on examples of their own choice)
    • run a model
    • know knowledge- and data-based modelling

    o   encoding causal relations (in the BN structure) & Learning (of BN parameters from data) . The combination of knowledge and data leads o probabilistic decision making.

    • know the sources of knowledge, data and uncertainties of a selected domain
    • know what features are the characteristic properties/parameters that describe a domain.

    o   The features can be obtained from measured sensor data or from calculated data (from models), also known as "soft sensor data", or from communicated (mobile) data.

    • How do hypotheses arise from features/characteristics? - by combining the features/characteristics/variables that constitute/manifest a hypothesis.

          --> see slide 9 in the lecture  slides (in English): Bayesian Networks for Decision Making in Automotive Applications - see

                                                                                              https://moodle.th-ab.de/course/view.php?id=5539#section-18

    Lecture ( 2023-05-04 - ,...) Bayesian Networks for Cognitive & Object-orriented Modelling (Decision Making) in Automotive ApplicationsDatei 

           --> see Note below: Explanation for decision-making through hypotheses from characteristics 

    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. 


    • can model and analyze sensor uncertainties.

    o   Why is it important to take the (sensor) uncertainties into account when making decisions? or What is the influence of uncertainties on the conclusion/decision?

    • understand which characteristics are used in the model hypotheses?

    o   The characteristics are obtained from measured sensor data or from calculated data (also known as "soft sensor data")

    • Which data types are used for modelling/learning:

    o   variable type (boolean, numbered, interval, labeled)

    • What is data labeling?

    o   annotate the state of a variable as e.g. boolean: true/false, or in example: maneuver class (variable “crossing the lane marking”: labeles: left/right/straight)

    • Classification of hypotheses under uncertainties?

    o   e.g. in an application such as detection of driving maneuvers

    o   based on the model structure and parameterization

    • Evidence as input to Bayesian network (decision making) model and interpretation of classification (decision) outcome
    • know tools for design, simulation and testing of decision-making models
    • understand for which applications decision-making methods (such as Bayesian networks) are appropriate and well suited (as discussed in the first lecture AND in eBook, Chapter 1: Introduction; 1.5 When to Use Probabilistic Nets)

     

    ------------- (Same content as above: in German///  Gleiche Fragen auf Deutsch, falls Sie Ihre Präsentation auf Deutsch vorbereiten oder präsentieren möchten) ------------------------------

    - Kognitive Hypothesen  unter Unsicherheiten in der gewählte BN Applikation (eigene oder gefundene Beispiele aus Internet/Literatur/etc.)
    - Kognitive Hypothesen  unter Unsicherheiten für Manöver Erkennung (siehe die Vorlesung)

    Wir diskutieren welche Fragen sind Prüfungsrelevant (mündliche Prüfung 20 Minuten für jede Kurse 7525 & 7540). Die Studierenden:

    • designen (erzeugen/lernen) ein Entscheidungsfindungsmodell (Bayes Netz) von einer Domäne (an Beispiele)
    • führen ein Modell aus
    • kennen wissen- und datenbasierten Modellierung
    • kennen die Quellen von Wissen, Data und Unsicherheiten einer Domäne
    • kennen was Merkmale sind (die Charkteristische Eigenschaften/Parameter, welche ein Domain beschreiben). Die Merkmale  wurden erhalten aus gemessene Sensor Daten oder von berechnete Daten (aus Modelen), auch bekannt als "soft sensor data", oder aus kommunizierte (mobile) Daten.
    • Wie entstehen Hypothesen aus Merkmalen? - durch Kombination der Merkmalen/Variablen, welche eine Hypothese darstellen. 
          --> siehe Folie 9 in der Vorlesungsfolien : Bayesian Networks for Decision Making in Automotive Applications - see

                                                                                              https://moodle.th-ab.de/course/view.php?id=5539#section-18

     PDF File: 

    Lecture ( 2023-05-04 - ,...) Bayesian Networks for Cognitive & Object-orriented Modelling (Decision Making) in Automotive ApplicationsDatei 


           --> siehe unten: Erklärung Video zur Entscheidungsfindung durch Hypothesen aus Merkmale

    • können Sensor-Unsicherheiten modellieren und analysieren. 
    • Warum ist es wichtig die (Sensor) Unsicherheiten auch bei Entscheidungsfindung zu berücksichtigen? bzw. Was ist der Einfluss von Unsicherheiten auf der Schlussfolgerung?
    • einordnen welche Merkmale werden in die Model-Hypothesen verwendet?  Die Merkmale werden erhalten von gemessene Sensor Daten  oder von berechnete Daten  (auch bekannt als "soft sensor data")
    • Welche Datentypen werden fürs Modellieren/Lernen verwendet: Variablen Typ (boolean, numbered, interval, labeled) 
    • Was ist "Data Labeling"? z.B. true/false, bzw. in Beispiel: Manöver Klass (Überquerung der Spurmarkierung: Links/Rechts/Gerade)
    • Klassifizierung von Hypothesen (z.B. in einer Anwendungen wie Erkennung von Fahrmanöver) unter Unsicherheiten
    • Evidenz als Input in Bayes Netz (Entscheidungsfindung) Model und Interpretation von Klassifikation (Entscheidung) Ergebnis.
    • kennen Werkzeuge für Design, Simulation und Testen von Entscheidungsfindungsmodelle
    • einordnen für welche Anwendungen Entscheidungsfindung Methoden (wie Bayes Netze) angebracht und gut geeignet sind
             (diskutiert in der erste Vorlesung UND in eBook, Chapter 1: Introduction; 1.5 When to Use Probabilistic Nets?) 

Questions to cover in your presentation: Cognitive Modeling & Learning of BN models for decision making
In English: Notes for the preparation of the exam on 19.01. & 22.01. 2024