cmu machine learning course

efficiency and classification effect.A popular approach is to use evolutionary algorithms to optimize feature ranges.A suitable K-value selection, through a variety of heuristic algorithms.Both classification and regression are weighted according to distance measurements, making the neighboring values more average.SummarizeKNN algorithm is the simplest and most effective algorithm for classifying data, which can help us to quickly understand the basic model of classification algorithm in superv

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 image recognition, semi-supervised

if you have a machine learning problem this problem has multiple special If you can ensure that these features are in a similar range, I mean to make sure that the values of the different features are within a similar range the gradient descent method can converge faster specifically if you have a problem with two features where X1 is the size of the house area Its value is between 0 and 2000 X2 is the n

A survey of data cleansing and feature processing in machine learning with the increase of the size of the company's transactions, the accumulation of business data and transaction data more and more, these data is the United States as a group buying platform of the most valuable wealth. The analysis and mining of these data can not only provide decision support for the development direction of the American

to use this feedback data to help us improve our current application. If the system is based on machine learning, log data can be used to improve the performance of applications. Of course, user behavior data is often noisy. We need to consider how to remove noise and Improve the Quality of log data. In another example, you may also know Amazon Mechanical Turk.

I. About the origins of the boosting algorithmThe boost algorithm family originates from PAC learnability (literal translation called Pac-Learning). This set of theories focuses on when a problem can be learned.We know that computable is already defined in computational theory, and that learning is what the PAC learnability theory defines. In addition, a large part of the computational theory is devoted to

-plane in a high-dimensional space separates the data points, which involves the mapping of non-linear data to high-dimensional to achieve the purpose of linear divisible data.Support Vector Concepts:The above sample map is a special two-dimensional situation, of course, the real situation may be many dimensions. Start with a simple understanding of what a support vector is at a low latitude. Can see 3 lines, the middle of the red line to the other tw

if you don't know why it works, you don't know when it's going to expire. Even a lockout watch can be correctly two times a day.” 5 Artificial intelligence is a long way off In fact, people use algorithms to select stocks is nothing new. The risk multiple factor model can be regarded as an algorithm for selecting stock. Of course, it works because of its use of factors, such as growth factors, scale factors, momentum factors, have a clear business ba

have been standing behind the scenes, and some things all the ins and outs only I know, because I and Dr. Huanghai, NetEase Cloud class, Professor Wunda and Coursera GTC translation platform, Deeplearning.ai official have had exchanges, so I still have to leave something as a description, Save everyone in the network every day noisy ah did not calm down to study seriously. As mentioned in this article, I have a chat record to support, some of the authorized information to retain the e-Mail recor

1 / 35 , the variation of each weight is +20,+50,+30, thus obtaining a new weight vector (70, 100, 80).The Delta-rule is given:In fact, this is the perception machine, which we have learned in Andrew Ng's course. The weighted vector obtained by iteration may not be perfect, but it should be a solution that makes the error small enough. If the

input data directly feedback to the model, the model must be immediately adjusted. Common application scenarios include dynamic 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

co-authored encyclopedia-style textbooks, not only in the field of numerical computing in detail, but also comes with high-quality source code, a lot of programs can be directly used. Of course, the book is very thick (1000+), but after reading through it should basically be able to deal with most of the problems encountered in the work of the numerical calculation.Gene H. Golub "Matrix computation"This should be done without too much introduction to

similarityAccording to the function and form similarity of the algorithm, we can classify the algorithm, for example, tree-based algorithm, neural network based algorithm and so on. Of course, the scope of machine learning is very large, and some algorithms are difficult to classify into a certain category. For some classifications, the same classification algor

Course Description:This lesson focuses on the things you should be aware of in machine learning, including: Occam's Razor, sampling Bias, and Data snooping.Syllabus: 1, Occam ' s razor.2, sampling bias.3, Data snooping.1, Occam ' s Razor.Einstein once said a word: An explanation of the data should is made as simple as possible, but no simpler.There are similar s