nyu machine learning course

A brief introduction to Learning _note1 against Sample machine Machine learning methods, such as SVM, neural network, etc., although in the problem such as image classification has been outperform the ability of human beings to deal with similar problems, but also has its inherent defects, that our training sets are fe

, people may have skin color, height, physique and ... Hey, I'm evil. And so on, are these features independent of each other? Of course not, such as the black average height is not white high, there are black people running ability and so on, characteristics and characteristics are related. But naive Bayesian sees them as independent. In principle, naive Bayes has an objective minimum error rate because it requires the least number of parameters. But

watch all the course videos at any time, download handouts and notes from Stanford CS229 course. This course includes homework and small tests, which mainly explain the knowledge of linear algebra, using the Octave library. Caltech learning from data at the California Institute of Technology: You can ta

1. The complete course of statistics all of statistics Carnegie Kimelon Wosseman 2. Fourth edition, "Probability Theory and Mathematical Statistics" Morris. Heidegger, Morris H.degroot, and Mark. Schevish (Mark j.shervish) 3. Introduction to Linear algebra, Gilbert. Strong--Online video tutorials are classic 4. "Numerical linear algebra", Tracy Füssen. Lloyd and David. Bao Textbooks suitable for undergraduates 5. Predictive data analysis of

introductory books. We recommend an article to further discuss this topic: "The best entry-level learning resources for machine learning". Related overview video: You can also watch some popular machine learning speeches. Example: Interview with Tom Angel El and Peter norv

. Classification model 1) training, testing. 2 Common methods: Naive Bayesian, maximum entropy, SVM. 6. Evaluation indicators 1) Accuracy rate Accuracy = (TP + TN)/(TP + FN + FP + TN) reflects the ability of the classifier to judge the whole sample--------------------positive judgment, negative judgment negative. 2) Accuracy rate Precision = tp/(TP+FP) reflects the proportion of the true positive sample in the positive case determined by the classifier 3) Recall rate Recall = tp/(TP+FN) reflec

formed a more perfect experience accumulation of the application scene. There are many applications in data mining that need to be developed, even if it is possible to dig out valuable patterns. Like Recommender systems, computer vision, and NLP, these values are known to be more fortunate than others. Write the Book of course everything to write, is there something in machine

the file name of the data to iris.csv. The Code is as follows: 1 Is it easy? Just 12 lines of code is enough. Next, let's test it. According to the figure above, when we input 5 3.3 1.4 0.2, the output should be Iris-setosa. Let's take a look: Check that at least one original data is input and the correct result is obtained. But what if we enter data that is not in the original dataset? Let's test two groups: From the data of the two images we posted earlier, the data we input does not exist

Machine learning Algorithms and Python Practice (ii) Support vector Machine (SVM) BeginnerMachine learning Algorithms and Python Practice (ii) Support vector Machine (SVM) Beginner[Email protected]Http://blog.csdn.net/zouxy09Machine lear