Artificial Intelligence
Bo Yuan, Ph.D.Professor
Shanghai Jiaotong University
Overview of Machine Intelligence• Knowledge-based rules (expert system, automata, …)
– Symbolic representation in logics (Deep Blue)
• Kernel-based heuristics (MDA, PCA, SVM, …) – Nonlinear connection for more representation (Neural Network)
• Inference (Bayesian, Markovian, …) – To sparsely sample for convergence (GM)
• Interactive and stochastic computing (uncertainty, heterogeneity) – To possibly overcome the limit of Turin Machine
InteractionsThe Framework to Study a System
Bottom-Up
Top-Down
How much can we represent and model a complex and evolving network ?
Low Complexity Solutions forHigh Complexity Problems
• Convexity • Stability (Metastability)• Sampling• Ergodicity• Convergence• Regularization• Software and Hardware
InteractionsThe Framework to Study a System
Bottom-Up
Top-Down
How much can we represent and model a complex and evolving network ?
Data Representation
Mathematical Foundation
MathematicalRepresentation
TypicalAlgorithm
AI-RelatedQuestion
Graph Graph Theory and Variable Reduction
OptimizationLiner Programming
Network Modularity and Organization
Logic Algebraic Logic Random Boolean Network, Automata
Network Structureand Attractors
Circuit Complex Number and Control Theory
LinearizationStability and control
Network Stability and Control
Reasoning Game Theory Evolutionary GameNash Equilibrium Markov Games
Inference Bayes Theorem Believe PropagationModel Searching Causality Inference
Discrete Stochastic
Markov-based Updating
ConvergenceMeta-stability
Evolution and Dynamics
Continuous Stochastic
Stochastic Differentials
Brownian integralsFokker-Planck
Network Dynamics and Control
Review of Lecture One• Overview of AI
– Knowledge-based rules in logics (expert system, automata, …) : Symbolism in logics– Kernel-based heuristics (neural network, SVM, …) : Connection for nonlinearity– Learning and inference (Bayesian, Markovian, …) : To sparsely sample for convergence– Interactive and stochastic computing (Uncertainty, heterogeneity) : To overcome the
limit of Turin Machine
• Course Content– Focus mainly on learning and inference– Discuss current problems and research efforts– Perception and behavior (vision, robotic, NLP, bionics …) not included
• Exam– Papers (Nature, Science, Nature Review, Modern Review of Physics, PNAS, TICS) – Course materials
Outline
• Knowledge Representation• Searching and Logics• Perceiving and Acting• Learning• Uncertainty and Inference
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