《认知神经科学》课程教学资源（参考文献）[Sporns, O., Chialvo, D. R., Kaiser, M., & Hilgetag, C. C.（2004）]Organization, development and function of complex brain networks

1. Introduction 2. Problem Definition 3. Related Work 4. Algorithm 5. Empirical Study 6. Conclusion

• Problem • System Modeling – Vacation model – Power efficiency – P-K formula of Mean delay • Performance Tradeoff • Conclusion

Research Background and Challenges Problem Statement Research Goal and Objectives Key Related Work Conceptual and Preliminary Design Detailed Design and Implementation Robust networking architecture Secure communication scheme System Evaluation and Test Results Evaluation of robust networking architecture Analysis of secure communication scheme Benchmarking Conclusion & Recommendations

背景 MDPs 强化学习问题 蒙特卡洛方法 (Monte Carlo Methods) 蒙特卡洛预测（Monte Carlo Prediction） 蒙特卡洛控制（Monte Carlo Control） 时序差分学习 (Temporal-Difference Learning) 时序差分预测（TD Prediction） 时序差分控制（TD Control） Sarsa：on-policy Q-learning : off-policy 策略梯度 (Policy Gradient) Monte-Carlo Policy Gradient Actor-Critic Policy Gradient 深度强化学习 Deep Q-Networks (DQN) Policy Gradients for Deep Reinforcement Learning

《Artificial Intelligence：A Modern Approach》教学资源（PPT课件，英文版）Chapter 14-Bayesian networks

Motivation: Network ≈ Wireless Interference Channel Approaches: NA in the middle, Precoding-Based NA PBNA Feasibility of PBNA Conclusion

北京航空航天大学：Graph Search & Social Networks

 4.1网络层的设计问题  4.2网际协议IP  4.3三级网络划分与无类别域间路由(CIDR)  4.4网际控制报文协议  4.5路由协议  4.6虚拟专用网VPN  4.7网络地址转换 NAT(Network Address Translation)  4.8下一代网际协议IPv6 (IPng)

2.1 数据通信基础  2.1.1 数据通信的理论基础  2.1.2 有导向的传输介质  2.1.3 无线通信 2.2 公共电话交换网络（PTSN) • 2.2.1 电话系统结构 • 2.2.2 本地环路 • 2.2.3 中继线 • 2.2.4 交换