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CS408: Advanced Artificial Intelligence (2018.01)
Sections
Course Introduction
Unit 1: Intelligent Agents and Problems of AI
Unit 2: Solving Problems by Searching
Unit 3: Logical Agents and Knowledge Representation
Unit 4: Learning
Unit 5: Philosophical Foundations of AI
Final Exam
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CS408: Advanced Artificial Intelligence (2018.01)
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CS408: Advanced Artificial Intelligence (2018.01)
Sections
Unit 4: Learning
4.2: Neural Network
4.2.6: Hopfield Network
Wolfram: "Hopfield Network"
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Wolfram: "Hopfield Network"
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Read this section, which presents the Hopfield network and the equations that describe it.
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https://web.archive.org/web/20150905193713/http://reference.wolfram.com/applications/neuralnetworks/NeuralNetworkTheory/2.7.0.html
link to open resource.
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Course Syllabus
Course Terms of Use
Unit 1 Learning Outcomes
The University of Memphis: Stan Franklin and Art Graesser's "Is It an Agent, or Just a Program?"
John Lloyd's"Intelligent Agents"
Stan Franklin's "A Cognitive Theory of Everything"
Wikipedia: "Artificial Intelligence"
Maurice Pagnucco's "Knowledge Representation and Reasoning"
Jussi Rintanen's "Planning"
Olivier Bousquet's "Introduction to Learning Theory"
Wikipedia: "Artificial Inelligence Approaches”
Michael Thielscher's "Systems with General Intelligence"
National Taiwan Normal University:Tsung-Che Chiang's "Vacuum Cleaner World"
Unit 2 Learning Outcomes
Cameron McLeman's "Graph"
Thomas Niemann's "Binary Tree"
Cameron McLeman's "Minimum Spanning Tree"
Thomas Niemann's "Binary Search Tree"
Thomas Niemann's "Red-Black Trees"
Thomas Niemann's "Skip List"
Wikipedia: "Depth-First Search"
Wikipedia: "Breadth-First Search"
Wikipedia: "Dijkstra's Algorithm"
Wikipedia: "Search Algorithm"
Zoubin Ghahramani's "Graphical Models: Parts 1-3"
Artificial Intelligence: A Modern Approach
: "Route Finding Agent"
Unit 3 Learning Outcomes
Wikipedia: "Logic Programming"
Alwen Tiu's "Introduction to Logic: Parts 1-3"
Paulo C.G. Costa and Kathryn B. Laskey's "Bayesian Networks"
Christopher Bishop's "Introduction to Bayesian Inference"
Wikipedia: "Hidden Markov Model"
Wikipedia: "Kalman Filter"
Wikipedia: "Decision Theory"
Maurice Pagnucco's "Knowledge Representation and Reasoning"
Massachusetts Institute of Technology: Randall Davis, Howard Shrobe, and Peter Szolovit's "What Is a Knowledge Representation?"
Marty Hall's "N-Queens Problem Demo"
Unit 4 Learning Outcomes
Wikipedia: "Machine Learning"
John Lloyd's "Intelligent Agents"
Sam Roweis' "Machine Learning, Probability, and Graphical Models"
Wolfram: "Introduction to Neural Networks”
Wolfram: "Feedforward Neural Networks"
Wolfram: "Radial Basis Function Networks"
Wolfram: "The Perceptron"
Wolfram: "Vector Quantization Networks"
Wikipedia: "Kernel Methods"
Wikipedia: "k-nearest Neighbor Algorithm"
Wikipedia: "Mixture Model"
Wikipedia: "Naive Bayes Classifier"
Wikipedia: "Decision Tree"
Mark Girolami's "Kernels and Gaussian Processes: Parts 1-3"
"Tic-Tac-Toe Demo"
Unit 5 Learning Outcomes
John Lloyd's "Intelligent Agents"
A. M. Turing's "Computing Machinery and Intelligence"
Paul M.B. Vitanyi's "Turing Machine"
Errol Martin's "Computability and Incompleteness"
Wikipedia: "Artificial Brain"
Wikipedia: "Physical Symbol System"
Igor Aleksander's "Machine Consciousness"
Massachusetts Institute of Technology: Marvin Minsky's "Emotion Machine"
CS408: Certificate Final Exam
CS408: Proctored Final Exam
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