SKIH 3013 || Undergraduate
Synopsis:
Pattern recognition techniques are used to automatically classify physical objects (handwritten characters, tissue samples, faces) or abstract multi-dimensional patterns (n points in d dimensions) into known or possibly unknown number of categories. This course will introduce the fundamentals of AI-based pattern recognition with examples from several application areas. The course will cover techniques for analyzing multi-dimensional data along with algorithms for projection, dimensionality reduction, clustering and classification. The course will present various approaches to classifier design so students can make judicious choices when confronted with real pattern recognition problems. It is important to emphasize that the design of a complete pattern recognition system for a specific application domain (e.g., business data) requires domain knowledge, which is beyond the scope of this course. Students will use available Python libraries / tools and will be expected to implement some algorithms to solve particular problems.
Learning Outcomes:
Upon completing this course, you will be able to:
Course Information:
Coordinator: Dr Azizi Ab Aziz (Assoc. Prof)
Level: Undergraduate (SKIH 3013)
E-mail : aziziaziz [at] uum [dot] edu [dot] my
Time : 1300-1420
Location: Sunday (BTM 010) & Wednesday (SOC Lab 03)
UUM Digital Learning Platform: UUM Online Learning
Announcement :
Syllabus & Lecture Notes:
Assignments/ Project:
Datasets
Reading Materials:
Pattern recognition techniques are used to automatically classify physical objects (handwritten characters, tissue samples, faces) or abstract multi-dimensional patterns (n points in d dimensions) into known or possibly unknown number of categories. This course will introduce the fundamentals of AI-based pattern recognition with examples from several application areas. The course will cover techniques for analyzing multi-dimensional data along with algorithms for projection, dimensionality reduction, clustering and classification. The course will present various approaches to classifier design so students can make judicious choices when confronted with real pattern recognition problems. It is important to emphasize that the design of a complete pattern recognition system for a specific application domain (e.g., business data) requires domain knowledge, which is beyond the scope of this course. Students will use available Python libraries / tools and will be expected to implement some algorithms to solve particular problems.
Learning Outcomes:
Upon completing this course, you will be able to:
- Explain the concept of pattern and the basic approach to developing pattern recognition & analysis.
- Prepare the appropriate pattern representation for different types of data to facilitate the recognition process.
- Analyze the data set in terms of abstraction using feature extraction and feature selection algorithms to reduce the dimensional of features.
- Formulate AI-based pattern recognition algorithms to detect and characterize patterns in real-world data
Course Information:
Coordinator: Dr Azizi Ab Aziz (Assoc. Prof)
Level: Undergraduate (SKIH 3013)
E-mail : aziziaziz [at] uum [dot] edu [dot] my
Time : 1300-1420
Location: Sunday (BTM 010) & Wednesday (SOC Lab 03)
UUM Digital Learning Platform: UUM Online Learning
Announcement :
Syllabus & Lecture Notes:
- Introduction to Pattern Recognition & Analysis ✔
- Feature Engineering
- Evaluation & Performance Metrics ✔
- Probability and Statistical (Parametric) Methods
- (Regression) ✔
- (Naive Bayes) ✔
- Distance-based (Non-parametric) Methods
- Decision Tree Methods
- Adaptive Learning Methods
- Issues and Future Trends in Pattern Recognition & Analysis ✔
Assignments/ Project:
- Assignment / Lab Work
- Self-Directed Study #1 ( 30th /10/23)✔
- Self-Directed Study #2 (28th / 11/ 2023)✔
- Assignment #1 (24th / Dec/ 2023)✔
- Assignment #2 (28th /01 / 2024)✔
- Group Project (5th / 02 / 2024)✔
Datasets
Reading Materials:
- Towards Building Voice-based Conversational Recommender Systems: Datasets, Potential Solutions, and Prospects.
- Artificial Intelligence, Machine Learning and Big Data in Finance Opportunities, Challenges and Implications for Policy Makers.
- Feature engineering strategies for credit card fraud detection
- Applying Naive Bayesian Networks to Disease Prediction: a Systematic Review
- Machine Learning Tool (WEKA)
- Data Mining Tool (Orange)
- Installing Python & Related Libraries (Python)