Module/Course Title: Artificial Intelligence

Module course code

KOMS120404

Student Workload
119 hours

Credits

3 / 4.5 ETCS

Semester

5

Frequency

Odd Semester

Duration

16

1

Type of course

Core Study Courses

Contact hours


40 hours of face-to-face (theoretical) class activity

Independent Study


48 hours of independent activity
48 hours of structured activities

Class Size

30

2

Prerequisites for participation (if applicable)

-

3

Learning Outcomes

  1. Students can demonstrate systematic thinking in analyzing and designing intelligent system solutions
  2. Students can apply effective methods in developing intelligent systems
  3. Students can create and evaluate intelligent systems
  4. Students can explain the method of representing knowledge on known information
  5. Students can apply intelligent agent theory to solve real problems
  6. Students can design problem solutions using an uninformed search approach
  7. Students can design solutions to problems with one of the search techniques with informed search
  8. Students can demonstrate reasoning with proportional logic and first order logic
  9. Students can use planning methods to solve problems
  10. Students can use the probability approach and Bayes rule to represent uncertain knowledge and do reasoning
  11. Students can explain the basic concepts of fuzzy logic
  12. Students can explain the basic concepts of machine learning

4

Subject aims/Content

This course studies how to make machines, in this case, computers, to imitate how humans think and act. It discusses various machine intelligence techniques and methods as well as their disadvantages, advantages, and applications. The study materials in this course include the concept of intelligent agents; problem-solving with search methods; knowledge and reasoning; planning; uncertain knowledge and logic; and learning. The materials included form the basis for all computer learning and place a foundation for the future of all complex decision-making. The activities conducted in this course include lectures and discussions synchronously (in class/lab/teleconference) and asynchronously (through e-learning media).

Study Material

MIDTERM TEST

FINAL TEST

  • Artificial Intelligence Overview;
  • Knowledge Representation.

 

Agent Based Intelligence

Troubleshooting with Search Approach

Uninformed Search

 

Informed Search

 

Genetic Algorithm in Searching

 

Reasoning: Proportional Logic

 

Reasoning: First Order Logic

 

Problem Solving with Planning

Uncertain Knowledge and Reasoning

Reasoning: Fuzzy Logic

 

Learning

Learning

Neural Networks for Learning

5

Teaching methods

-

6

Assesment Methods

  • Presence;
  • UTS work/tasks.

7

This module/course is used in the following study programme/s as well

Computer Science Study Programme

8

Responsibility for module/course

  • Ni Putu Novita Puspa Dewi, S.Kom., M.Cs.
  • NIDN : 199410032020122000

9

Other Information

This is a general reading list. More detailed lists for individual components will follow later.

  1. Russel, Stuart J., Peter Norfig, "Artificial Intelligence - A Modern Approach 3rd Edition", Prentice-Hall International, 2010. 2.
  2. Luger, George F., "Artificial Intelligence - Structures and Strategies for Complex Problem Solving", Addison-Wesley, 2009. 3.
  3. Kusumadewi, S., "Artificial Intelligence: Teknik dan Aplikasinya", Yogyakarta: Graha Ilmu, 2003.
  4. Dewi, N. P. N. P., & Nugroho, R. A. (2021, January 31). Optimasi General Regression Neural Network dengan Fruit Fly Optimization Algorithm untuk Prediksi Pemakaian Arus Listrik pada Penyulang. KOMPUTASI. Retrieved January 25, 2022, from https://journal.unpak.ac.id/index.php/komputasi/article/view/2144
  5. Purwanta, I. P. B. D., Adi, C. K., & Dewi, N. P. N. P. (2020). Backpropagation neural network for book classification using the image cover. International Journal of Applied Sciences and Smart Technologies. Retrieved January 25, 2022, from https://ejournal.usd.ac.id/index.php/IJASST/article/view/2653
  6. Gensler A, Henze J, SickRaabe B, Raabe N. Deep Learning for Solar Power Forecasting – An Approach Using Autoencoder and LSTM Neural Networks. In: 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2016). Budapest, Hungary: IEEE; 2016. p. 2858–65.

  7. Dewi, N.P. and Purwanta, I.P. (2021) ‘Big Data for Indonesian marine fisheries’, Proceedings of the 4th International Conference on Innovative Research Across Disciplines (ICIRAD 2021) [Preprint]. doi:10.2991/assehr.k.211222.040.

  8. Dewi, N.P., Kertiasih, N.K. and Sintiari, N.L. (2022) ‘Modifikasi fruit fly optimiziation algorithm untuk optimasi general regression neural network  Pada Kasus prediksi time-series’, Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI), 11(3), pp. 192–204. doi:10.23887/janapati.v11i3.54521.

Students should have access to at least the most recent issues of the following journals: IEEE, Springer, or any journal related to the topic of Artificial Intelligent. Many relevant publications can be downloaded free of charge from the websites of mdpi.com. Students are highly recommended to read articles from websites such as machinelearningmastery.com, towardsdatascience.com, and aitopics.org.