1. 這是對machine learning的定義，讓我可以從更廣義的角度來看何謂ML
Machine Learning is programming computers to optimize a performance criterion using example data or past experience. We have a model defined up to some parameters and learning is the execution of a computer program to optimize the parameters of the model using the training data or past experience. The model may be predictive to make predictions in the future, or descriptive to gain knowledge from data, or both
ML使用統計的理論來建立數學模型，並可以應用於不同領域問題，包含Data Mining、AI等
2. 一直以來我們幾乎都講求predictive accuracy，但我覺得這是應用他人的演算法才如此
如果當你自行設計新的演算法時，還要考慮space and time complexity
3. ML主要可分為下面五類
- Association
- Classification (supervised learning)
- Regression (supervised learning)
- Unsupervised Learning
- Reinforcement Learning
4. ML的應用如下…
- Association Rule：Basket Analysis
- Pattern Recognition：optical character recognition、face recognition、medical diagnosis、speech recognition
- Knowledge Extraction: Learning a rule from data
- Outlier detection
- Robotics
- Image Compression
- …太多了
5. Supervised Learning：the task is to learn the mapping from the input to the output
Unsupervised Learning：find the regularities in the input. There is a structure to the input space such that certain patterns occur more often than others, and we want to see what generally happens and what does not. In statistics, this is called density estimation. One method for density estimation is clustering
6. Reinforcement Learning： find policy that is the sequence of correct actions to reach the goal
one factor that makes reinforcement learning harder is when the system has unreliable and partial sensory information. we can not decide in a partially observable state → using multiple agents that interact and cooperate to accomplish a common goal.For example, robots playing soccer
7. In statistics, going from particular observations to general descriptions is called inference and learning is called estimation. Classification is called discriminant analysis in statistics. In engineering, classification is called pattern recognition and the approach is nonparametric and much more empirical
(終於解答我之前一直confuse的地方－ 「pattern recognition」的意義)
8. Relevant Resource
Journal
- Machine Learning
- Journal of Machine Learning Research
- Neural Computation
- Neural Networks
- IEEE Transactions on Neural Networks
- IEEE Transactions on Pattern Analysis
- Machine Intelligence
- Artificial Intelligence
- Pattern Recognition
- Fuzzy Logic
- Data Mining and Knowledge Discovery
- IEEE Transactions on Knowledge and Data Engineering
- ACM SIGKDD
Conference
- Neural Information Processing Systems (NIPS)
- Uncertainty in Artificial Intelligence (UAI)
- International Conference on Machine Learning (ICML)
- European Conference on Machine Learning (ECML)
- Computational Learning Theory (COLT)
- Internatioal Joint Conference on Artificial Intelligence (IJCAI)
- ......................
Dataset (DataBase)
- UCI Repository : http://www.ics.uci.edu/~mlearn/MLRepository.html (超多dataset)
- UCI KDD Archive：http://kdd.ics.uci.edu/summary.data.application.html (這裡也有一些，而且已分類好了，其實跟上面那一個為同個網站)
- Delve：http://www.cs.toronto.edu/~delve/ (有一些dataset及software可以使用)
下一章 Supervised Learning
沒有留言:
張貼留言