CSBP411: Machine Learning
|Description:||Adaptive technologies used in Speech and Image Processing; Bioinformatics systems; Web search and text classification; Feature extraction; Decision trees; Neural networks; Genetic algorithms; Bayesian learning; Reinforcement Learning.|
|Text Book: ||I. H. Witten & E. Frank. Data Mining: Practical Machine Learning Tools and Techniques, 2nd Ed. Morgan Kaufmann, 2005. |
|Coordinator: ||Nazar Zaki|
|Topics Outline: |
- Introduction to Machine Learning (ML): Simple examples, fielded applications, ML and statistics.
- Input: Concepts, instances, and attributes.
- Output: Knowledge representation.
- Algorithms: The basic methods.
- Credibility: Evaluating what's been learned.
- Implementations: Real machine learning schemes.
- Describe the main components of a machine learning system.
- Design training sets and testing sets for machine learning tasks.
- Apply machine learning tools.
- Evaluate different machine learning techniques in terms of their applicability to different Machine Learning problems.
- Compare various classification algorithms.
|Mapping of Topics Outline to Outcomes |
|Pre-requisite||CSBP301: Artificial Intelligence|