STIN 2054 || Undergraduate
Synopsis:
Neural networks provide a model of computation drastically different from traditional computers. Typically, neural networks are not explicitly programmed to perform a given task; rather, they learn to do the task from examples of desired input/output behavior. The networks automatically generalize their processing knowledge into previously unseen situations, and they perform well even when the input is noisy, incomplete or inaccurate. These properties are well-suited for modeling tasks in ill-structured domains such as face recognition, speech recognition and motor control. This course will cover basic neural network architectures and learning algorithms, for applications in selected domains. Two forms of learning will be introduced (supervised and unsupervised learning) and applications of these will be discussed. The students will have a chance to try out several of these models on practical problems.
Learning Outcomes:
At the end of the module, the student will be able to demonstrate:
Course Information:
Coordinator: Dr Azizi Ab Aziz
Level: Undergraduate (Level 2)
E-mail : aziziaziz [at] uum [dot] edu [dot] my
Time : 1130-1320/ (Monday & Thursday)
Location: UUM Main Campus DKG 4/5)
Consultation Time: 1500-1700 (Tuesday) / or any appropriate schedule agreed by both parties
UUM Digital Learning Platform: UUM Online Learning
Announcement :
* Mini-project - to be submitted on 27th Jan 2023
* 3rd Assignment - 20th Jan 2023
Syllabus:
Assignments/ Project:
Programming / Source Code (Python Programming)
Software / Tools:
Neural networks provide a model of computation drastically different from traditional computers. Typically, neural networks are not explicitly programmed to perform a given task; rather, they learn to do the task from examples of desired input/output behavior. The networks automatically generalize their processing knowledge into previously unseen situations, and they perform well even when the input is noisy, incomplete or inaccurate. These properties are well-suited for modeling tasks in ill-structured domains such as face recognition, speech recognition and motor control. This course will cover basic neural network architectures and learning algorithms, for applications in selected domains. Two forms of learning will be introduced (supervised and unsupervised learning) and applications of these will be discussed. The students will have a chance to try out several of these models on practical problems.
Learning Outcomes:
At the end of the module, the student will be able to demonstrate:
- describe the fundamental concepts in neural networks.
- differentiate between supervised and unsupervised learning neural networks.
- formulate specified intelligent solutions to identified problems
- compare out how various neural networks algorithms work.
Course Information:
Coordinator: Dr Azizi Ab Aziz
Level: Undergraduate (Level 2)
E-mail : aziziaziz [at] uum [dot] edu [dot] my
Time : 1130-1320/ (Monday & Thursday)
Location: UUM Main Campus DKG 4/5)
Consultation Time: 1500-1700 (Tuesday) / or any appropriate schedule agreed by both parties
UUM Digital Learning Platform: UUM Online Learning
Announcement :
* Mini-project - to be submitted on 27th Jan 2023
* 3rd Assignment - 20th Jan 2023
Syllabus:
- Introduction to Artificial Neural Networks ✓
- Perceptron ✓
- Multi-layer Perceptron ✓
- Radial-Basis Function ✓
- Unsupervised Neural Networks ✓
- Deep Learning - Convolution Neural Networks ✓
- Deep Learning - Recurrent Networks ✓
- Selected Case Studies / Applications ✓
- Issues & Progress in Neural Networks ✓
Assignments/ Project:
- Assignment #1 (submission - 04/11/2022) ✓
- Self-Directed Group Activity #1 (submission - 09/11/2022) ✓
- Self-Directed Group Activity #2 (submission - 08/12/2022) ✓
- Assignment #2 (submission - 20/12 /2022) ✓
- Assignment #3 (submission - 20 /01 /2023)<<
- Project ( submission -27 /01 /2023) <<
Programming / Source Code (Python Programming)
- Practical Codes for Chap 02 ✓
- Practical Codes for Chap 03 ✓
- Practical Codes for Chap 04 ✓
- Practical Codes for Chap 05 ✓
- Practical Codes for Chap 06 ✓
- Practical Codes for Chap 07 ✓
- Practical Codes for Chap 08 ✓
Software / Tools: