Neural Network :
Idea : Use neurons as the hyperplane to separate trainning example by class.
How to : Using Back-Propagation Algorithm to train the neural network .
Computational Complexity :
1. The computational model of the turing machine.
All problems that can be solved by existing machines can be also solved by turing machines.
2. The concept of the P , NP , NPC , NP-hard
P : Collect the problems that can be solved in polynomial time by TM.
NP: Collect the problems that can be solved in polynomial time by NTM (recall decision tree , guess and verify !)
NPC: Collect the problems in NP that every problems in NP can be reduced to them.
NP-hard: Collect the problems that every problems in NP can be reduced to them , and they maybe are not in NP.
Artificial Intelligence :
1.How to design an intelligent agent ? Consider the performance of the agent to solve problems .
2.Local Search method can be a good search method to solve NP problems .
Multi-Agent Meeting :
1. Recommender System that was implemented by using collaborative filtering algorithm.
2. Collaborative filtering algorithm :
Idea : Transform the rating data in to the rating matrix , and use the matrix as the computation basis to find each one’s most similar users and using those users’ rating data as the prediction of our target users .
CF can be classified into 3 main class :
1. User-based : row vectors
2. Item-based : column vectors
3. Unifying User-based and Item-based method : row vectors & column vectors (Can handle sparsity issues)
Comments : Using cluster algorithm can handle scalability issues.