cmu machine learning course

difference learning (temporal difference learning)In the case of enterprise Data application, the most commonly used is the model of supervised learning and unsupervised learning. In the field of image recognition, semi-supervised learning is a hot topic because of the larg

there was a profound idea. However, the Bayesian method swept through probability theory and applied it to various problem fields. The shadows of Bayesian methods can be seen in all places where Probability Prediction is needed, bayesian is one of the core methods of machine learning. The profound reason behind this is that the real world itself is uncertain, human observation capabilities are limited (oth

automated Systems developed with automated Python (using Python to build a Machine Learning system) Building automated Machine Learning automated Systems developed with automated Python, developed 2nd automated Edition (build a Machine

systems and robot control. Common algorithms include q-learning and time difference learning (temporal difference learning). In the case of enterprise Data application, the most commonly used is the model of supervised learning and unsupervised learning. In the field of ima

lot of things, such as:1. Collect more data and let us have more spam and non-spam samples2. Message-based routing information develop a complex set of features3. The development of a series of complex features based on the message body information, including the processing of the truncated words4. Develop complex algorithms for detecting deliberate spelling errors (writing watch as W4tch)Among the options above, it is very difficult to decide which item to spend time and effort on, making wise

Because there is a very detailed online blog, so this section will not write their own, write can not write others so good and thorough.jerrylead Support Vector Machine series:Support Vector Machine (i): http://www.cnblogs.com/jerrylead/archive/2011/03/13/1982639.htmlSupport Vector Machine (ii): http://www.cnblogs.com/jerrylead/archive/2011/03/13/1982684.htmlSupp

. Application scenarios of Naive Bayes An important application of machine learning is automatic document classification, while Naive Bayes is a common algorithm for document classification. The basic step is to traverse and record the words that appear in the document, and use the appearance or absence of each word as a feature. In this way, there are as many features as the number of words in the document

Learning Guide for machine learning beginners (experience sharing)2013-09-21 14:47I computer research two, the professional direction of natural language processing, individuals interested in machine learning, so began to learn. So, this guy is a rookie ... It is because of

MIT's algorithm introduction Open class, many years ago saw, has not insisted to see, recently looking for summer internship, interview is basically algorithm, had to take time to brush Leetcode, also through this opportunity hope to see this video, the algorithm of the basic skills to play a solid, this public class is still quite good.Before learning other things, remember a lot of notes, and finally lost, want to look at the time has not been found

Machine learning Notes (i)Today formally began the study of machine learning, in order to motivate themselves to learn, but also to share ideas, decided to send their own experience of learning to the Internet to let everyone share.Bayesian learningLet's start with an exampl

be struggling. So the bean leaf emphasizes the importance of a good foundation. Once you have mastered the basics of mathematics, your understanding of these models can easily transcend the formula itself.The difference between deep knowledge and shallow knowledgeBean leaves think that when we learn knowledge, we should learn to differentiate, what is deep knowledge (knowledge), what is shallow knowledge (shallow knowledge).Some knowledge is shallow knowledge, only need to remember to know. But

-party library, without organic integration, the corresponding learning costs will be higher. Python is faster than R. Python can directly deal with the data on the G, R No, r analysis data need to first through the database to transform big data into small data (through GroupBy) to the R for analysis, so R can not directly analyze the behavior of the list, can only analyze statistical results. Python's advantage lies in its glue language characterist

Course Address: Https://class.coursera.org/ntumltwo-002/lectureImportant! Important! Important!1. Shallow-layer neural networks and deep learning2. The significance of deep learning, reduce the burden of each layer of network, simplifying complex features. Very effective for complex raw feature learning tasks, such as machine