Download Page 1 of 3 COMP 3170 Artificial Intelligence and Machine Learning

Title (Units):
COMP 3170 Artificial Intelligence and Machine Learning (3, 2, 1)
Course Aims:
To introduce the basic concepts, theories and state-of-the-art techniques of artificial intelligence, and
in particular, machine learning. To give students practical insights into the current development of
the field.
Prerequisite:
COMP1210 Data Structures and Algorithms
MATH 1130 Discrete Structures
STAT 1210 Probability and Statistics
Learning Outcomes (LOs):
Upon successful completion of this course, students should be able to:
No.
1
2
3
4
5
6
Learning Outcomes (LOs)
Knowledge
Explain the capabilities, strengths and limitations of various artificial intelligence and machine learning techniques
Explain various AI and machine learning algorithms and their applications
Describe learning systems and leaning algorithms
Professional Skill
Have the ability to implement selected AI and machine learning algorithms to solve real world problems
Transferable Skill
Have the ability to understand complex ideas and relate them to specific situations, the ability to evaluate available
learning methods and select those appropriate to solve a given task
Attitude
Reflect that machine intelligence is challenging and that solving real world problems require multidisciplinary effort
Calendar Description:
This course aims to introduce the principles and fundamental techniques of artificial intelligence, and
in particular, machine learning. Students will learn the fundamentals and state-of-the-art techniques
and acquire practical insights into the current development of this field.
Assessment:
No.
Assessment
Methods
Weighting
1
Continuous
Assessment
30%
2
Examination
70%
Remarks
Assignments and mini-projects will be used to evaluate students’ understanding
of basic concepts and to assess their ability to apply learning theory to solve real
world problems (LO 5)
Examination will be used to assess students’ overall understanding of various
artificial intelligence and machine learning algorithms, their applications, as well
as their capabilities, strengths and limitations.
Learning Outcomes and Weighting:
Content
I. Introduction to AI
II. Search
III. Knowledge Representations and Reasoning
IV. Concept Learning and the General-to-Specific Ordering
V. Decision Tree Learning
VI. Artificial Neural Networks
VII. Evaluating Hypotheses
VIII. Bayesian Learning
IX. Genetic Algorithms
X. Reinforcement Learning
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LO No.
1, 3, 6
2, 3
3, 4
1, 2, 3
3, 4, 5
1, 3, 5, 6
3, 4, 6
2, 3, 4, 5
1, 2, 3
2, 3, 5, 6
References:
Tom M. Mitchell, Machine Learning, McGraw-Hill International Editions, 1997.
Stuart Russell and Peter Norvig, Artificial Intelligence, A Modern Approach, Prentice Hall, 2nd
Edition, 2003.
Michael Negnevitsky, Artificial Intelligence: A Guide to Intelligent Systems, Addison Wesley, 2nd
Edition, 2005.
Simon Haykin, Neural Networks: A Comprehensive Foundation, Prentice Hall, 2nd Edition, 1999.
Judea Pearl, Causality: Models, Reasoning, and Inference., Cambridge University Press, 1st Edition,
2001.
Ryszard S. Michalski, Ivan Bratko and Miroslav Kubat, Machine Learning and Data Mining:
Methods and Applications, Chichester, West Sussex, England; New York: J. Wiley, 1998.
George F. Luger and William A. Stubblefield, Artificial Intelligence – Structures and Strategies for
Complex Problem Solving, Benjamin/Cummings, 5th Edition, 2005.
Trevor Hastie, Robert Tibshirani and Jerome Friedman, The Elements of Statistical Learning: Data
Mining, Inference, and Prediction, New York: Springer, 2001.
Course Content in Outline:
Topic
I.
Introduction to AI
A. History
B. Applications
C. Prospect
II.
Search
A. Uninformed search
B. Heuristic search
C. Constraint satisfaction search
III.
Knowledge Representations and Reasoning
A. Prepositional and predicate logic
B. Other representation techniques
C. Uncertainty knowledge and reasoning
IV.
Concept Learning and the General-to-Specific Ordering
A. Concept learning as search
B. Search techniques
C. Version spaces and the candidate-elimination algorithm
V.
Decision Tree Learning
A. Basic decision tree learning algorithm
B. Hypothesis space search and inductive bias in decision tree learning
C. Issues in decision tree learning
VI.
Artificial Neural Networks
A. Feed-forward neural networks
B. Recurrent neural networks
C. Models and algorithms
VII.
Evaluating Hypotheses
A. Estimating hypothesis accuracy
B. Basics of sampling theory
C. A general approach for deriving confidence intervals
D. Comparing learning algorithms
VIII.
Bayesian Learning
A. Bayes theorem and concept learning
B. Maximum likelihood and least-squared error hypotheses
C. Minimum description length principle
D. Bayes optimal classifier and gibbs algorithm
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E. Bayesian belief networks
IX.
Genetic Algorithms
A. Genetic algorithms
B. Hypothesis space search
C. Genetic programming
D. Models of evolution and learning
X.
Reinforcement Learning
A. Q-learning
B. Temporal difference learning
C. Relationship to dynamic programming
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