1. Artificial Intelligence
Bo Yuan, Ph.D.
Professor
Shanghai Jiaotong University

2. 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

3. Interactions The Framework to Study a System
Top-Down
Bottom-Up

4. How much can we represent and model a complex and evolving network ?

5. Low Complexity Solutions for High Complexity Problems
Convexity
Stability (Metastability)
Sampling
Ergodicity
Convergence
Regularization
Software and Hardware

6. Interactions The Framework to Study a System
Top-Down
Bottom-Up

7. How much can we represent and model a complex and evolving network ?

8. Mathematical Foundation Continuous Stochastic
Data Representation
Mathematical Foundation
Mathematical
Representation
Typical
Algorithm
AI-Related
Question
Graph
Graph Theory and Variable Reduction
Optimization
Liner Programming
Network Modularity and Organization
Logic
Algebraic Logic
Random Boolean Network, Automata
Network Structure
and Attractors
Circuit
Complex Number and Control Theory
Linearization
Stability and control
Network Stability and Control
Reasoning
Game Theory
Evolutionary Game
Nash Equilibrium
Markov Games
Inference
Bayes Theorem
Believe Propagation
Model Searching
Causality Inference
Discrete Stochastic
Markov-based Updating
Convergence
Meta-stability
Evolution and
Dynamics
Continuous Stochastic
Stochastic Differentials
Brownian integrals
Fokker-Planck
Network Dynamics and Control

9. Review of Lecture One Overview of AI Course Content
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

10. Outline Knowledge Representation Searching and Logics
Perceiving and Acting
Learning
Uncertainty and Inference