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COMP 657: Artificial Intelligence: Principles and Techniques Report a Broken Link

COMP 657: Artificial Intelligence: Principles and Techniques provides a comprehensive, graduate-level introduction to artificial intelligence, emphasizing advanced topics such as advanced search, reasoning and decision-making under uncertainty, and machine learning.

Unit 1: Informed Search and Online Search

Readings
Hedberg, S. R. (2005). Evolutionary computing: The rise of electronic breeding. IEEE Intelligent Systems, 20(6), 12–15. https://doi.org/10.1109/MIS.2005.104
AIMA Exercises
1.14 (optional)
2.2
2.5
3.11
3.36
3.37
3.39
3.40 (optional)
4.1
4.16
Resources
AITopics [website]
Russell, S., & Norvig, P. (2022, August 22). Artificial intelligence: A modern approach, 4th US ed. [website]. https://aima.cs.berkeley.edu/index.html

Unit 2: Games and Adversarial Search

Supplemental Readings
AITopics: Games [webpage]
AIMA Exercises
5.6
5.8
Resources
Association for Computing Machinery [website]
Association for the Advancement of Artificial Intelligence [website]
Google. (n.d.) AlphaGo. Google DeepMind. https://deepmind.google/technologies/alphago/
IBM. (n.d.). Deep Blue. https://www.ibm.com/history/deep-blue

Unit 3: Constraint Satisfaction Problems

AIMA Exercises
6.1
6.11
6.19

Unit 4: Logic, Inference, and Ontology

AIMA Exercises
7.1
7.5
7.30
8.30
9.3
9.7
9.8
AIMA Exercises (github)
Resources
SWI-Prolog [website]

Unit 8: Complex Decision-Making

AIMA Exercises
16.4
16.22
17.6
17.17
17.19

Unit 9: Machine Learning Basics

Resources
scikit-learn [website]
Waikato University. (n.d.). Weka 3: Machine learning software in Java. Machine Learning at Waikato University. https://www.cs.waikato.ac.nz/ml/weka/

Unit 10: Computational Learning Theory and Statistical Learning

AIMA Exercises
20.11

Unit 11: Reinforcement Learning and Deep Learning

Supplemental Readings
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press. https://www.deeplearningbook.org
Resources
Keras [website]
MXNet [github]
PyTorch [website]
PyTorch [github]
TensorFlow [website]
TensorFlow [github]

Unit 12: Natural Language Processing

Supplemental Readings
Jurafsky, D., & Martin, J. H. (2023, January 7). Speech and language processing (3rd ed. draft). Prentice Hall. https://web.stanford.edu/~jurafsky/slp3/
AIMA Exercises
23.7
23.16
Resources
FrameNet [website]
NLTK [website]

Unit 14: AI in Business Intelligence and E-Learning

Resources
International Journal of Artificial Intelligence in Education [website]

Assignments

Resources
IEEE. (n.d.). Manuscript templates for conference proceedings. https://www.ieee.org/conferences/publishing/templates.html