Download Grading Policy - Al-Balqa` Applied University

Al-Balqa' Applied University/Faculty of Engineering Technology
Computer Engineering Department
Machine learning
30102425
Spring 2013/2014
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Course description:
Machine Learning is concerned with computer programs that automatically improve their
performance through experience. This course covers the theory and practice of machine learning
from a variety of perspectives. We cover topics such as learning decision trees, neural network
learning, statistical learning methods, genetic algorithms, Bayesian learning methods and
reinforcement learning.
Grading Policy:
Quizzes or Projects
10%
1st Midterm Exam
20%
2nd Midterm Exam
20%
Final Exam
50%
Textbook:
Machine Learning, Tom Mitchell, McGraw Hill, 1997.
References:
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T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning: Data Mining,
Inference, and Prediction (Springer Series in Statistics), Springer-Verlag, October 2001.
C. Bishop, Neural Networks for Pattern Recognition, Oxford University Press, January 1996.
Course outline:
Week
1
2,3,4
5
6
7
8
9
10
Topics
Introduction
Artificial neural networks: basic concept, perceptron, backpropagation.
Unsupervised learning: self-organization and Hebbian learning.
Review & exam 1
Decision tree learning
Bayesian learning
Genetic algorithm
K-Nearst-Neighbor
Review & exam 2
11, 12
13
14
Fuzzy logic
Support vector machine
Review