dudung / sk5009-01-2024-1

Advanced Artificial Intelligence course in 2024-1 semester
MIT License
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Assignment 02+n -- Course introduction and RBL topics #2

Open dudung opened 2 weeks ago

dudung commented 2 weeks ago

materials

  1. Introduction: Artificial Intelligence Systems, Intelligent agents
  2. Basic Theory of Probabilistic Reasoning
  3. Fuzzy Logic 1: Definition of fuzzy logic, getting to know the difference between classical sets (Classical Sets) and fuzzy logic (Fuzzy Sets), Concept of the relationship between classical logic and fuzzy logic (Classical and Fuzzy Relations)
  4. Fuzzy Logic 2: Membership function and Defuzzification, Fuzzy Rule-Based System
  5. Fuzzy Logic 3: Fuzzy Decision Making, Applications of Fuzzy Logic
  6. Artificial Neural Networks (Artificial Neural Networks) 1: Working principles.
  7. Artificial Neural Networks 2: Introduction and Rosenblatt's Perceptron
  8. Artificial Neural Networks 3: Model Building through Regression, The Least-Mean-Square Algorithm
  9. Artificial Neural Networks 4: Multilayer Perceptrons
  10. Artificial Neural Networks 5: Radial-Basis Function Networks
  11. Artificial Neural Networks 6: Support Vector Machines
  12. Artificial Neural Networks 7: Self-Organizing Maps
  13. Research Based Learning 1: RBL project proposal presentation
  14. Research Based Learning 2: RBL project guidance discussion in class or computer lab, progress report presentation
  15. Research Based Learning 3: RBL project guidance discussion in class or computer lab, progress report presentation
  16. Research Based Learning 4: RBL project guidance discussion in class or computer lab, progress report presentation, final presentation

learning outcomes

  1. Students are able to understand how the basic ideas of artificial intelligence systems are designed.
  2. Students are able to understand what methods have been developed in artificial intelligence systems and what they have been applied for so far.
  3. Students are able to master at least two methods in artificial intelligence systems, namely classification problems using fuzzy logic, and learning methods using artificial neural networks.
  4. Students are able to create computer programs and understand them so they can be applied to systems.

methods and modalities

  1. Methods: Lectures, discussions, research/problem/case study based learning, literature studies, group/independent work, presentations, practice.
  2. Modalities: Offline/online/hybrid, synchronous and asynchronous.

tasks (6 hours)

fayahmad07 commented 2 weeks ago

Student ID : 20923301 https://medium.com/@fayahmad07/smart-farming-with-fuzzy-logic-an-rbl-project-proposal-for-optimizing-plant-nutrition-ph-and-00d7ed716f5a

ilonajoan07 commented 2 weeks ago

Ilona JM (20924008) https://medium.com/@ilona.10520059/advanced-ai-rbl-topic-ideas-5da4d011b830

ikhsanmn commented 1 week ago

Ikhsan MN student ID (20923305) https://medium.com/@ikhsanmnoor/ann-model-for-predict-corrosion-inhibitor-efficiency-dd9555c51c6b

ghinaalamsyahh commented 1 week ago

Ghina Hanifah Alamsyah // Student ID: 10221037 https://medium.com/@ghinaalamsyah70/some-rbl-topics-to-consider-22a05647ed97