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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.
Credit Hours.:3
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:
  1. Introduction to Machine Learning (ML): Simple examples, fielded applications, ML and statistics.
  2. Input: Concepts, instances, and attributes.
  3. Output: Knowledge representation.
  4. Algorithms: The basic methods.
  5. Credibility: Evaluating what's been learned.
  6. Implementations: Real machine learning schemes.
Outcomes:
  1. Describe the main components of a machine learning system.
  2. Design training sets and testing sets for machine learning tasks.
  3. Apply machine learning tools.
  4. Evaluate different machine learning techniques in terms of their applicability to different Machine Learning problems.
  5. Compare various classification algorithms.
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Pre-requisiteCSBP301: Artificial Intelligence
Volume of the Course that Contributes to CIT Students Outcomes(SOs)
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