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cs229.stanford.edu

CS 229: Machine Learning

The first discussion section will be held on Friday 9/26, in the NVIDIA auditorium, from 4:15 - 5:05 pm. It will cover some materials in linear algebra useful for this course. The first class will be held at 9:00 am on Monday 9/22, in the NVIDIA auditorium. We look forward to seeing you there! Data for problem set 1 can be downloaded here q1x.dat. The project guideline has been released. Please check here. The suggested projects list has been released. Please check here. Materials from the Matlab tutorial.

http://cs229.stanford.edu/

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CS 229: Machine Learning | cs229.stanford.edu Reviews
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The first discussion section will be held on Friday 9/26, in the NVIDIA auditorium, from 4:15 - 5:05 pm. It will cover some materials in linear algebra useful for this course. The first class will be held at 9:00 am on Monday 9/22, in the NVIDIA auditorium. We look forward to seeing you there! Data for problem set 1 can be downloaded here q1x.dat. The project guideline has been released. Please check here. The suggested projects list has been released. Please check here. Materials from the Matlab tutorial.
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1 machine learning
2 announcements
3 problem set 1
4 has been released
5 q1ydat
6 q2xdat
7 q2ydat
8 are now up
9 problem set 2
10 the practice midterm
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machine learning,announcements,problem set 1,has been released,q1ydat,q2xdat,q2ydat,are now up,problem set 2,the practice midterm,is now up,midterm solutions,are posted,course information,instructor,andrew ng,lectures,nvidia auditorium,scpd,home page
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CS 229: Machine Learning | cs229.stanford.edu Reviews

https://cs229.stanford.edu

The first discussion section will be held on Friday 9/26, in the NVIDIA auditorium, from 4:15 - 5:05 pm. It will cover some materials in linear algebra useful for this course. The first class will be held at 9:00 am on Monday 9/22, in the NVIDIA auditorium. We look forward to seeing you there! Data for problem set 1 can be downloaded here q1x.dat. The project guideline has been released. Please check here. The suggested projects list has been released. Please check here. Materials from the Matlab tutorial.

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CS 229: Machine Learning Final Projects, Autumn 2014

http://cs229.stanford.edu/projects2014.html

CS 229 Machine Learning. Final Projects, Autumn 2014. Nonlinear Reconstruction of Genetic Networks Implicated in AML. Aaron Goebel, Mihir Mongia . [pdf]. Can Machines Learn Genres. Aaron Kravitz, Eliza Lupone, Ryan Diaz. [pdf]. Identifying Gender From Facial Features. Abhimanyu Bannerjee, Asha Chigurupati. [pdf]. Abhinav Rastogi, Sevy Harris. [pdf]. Intensity prediction using DYFI. Abhineet Gupta. [pdf]. Artificial Intelligence on the Final Frontier - Using Machine Learning to Find New Earths. Alejandro ...

2

CS 229: Machine Learning Final Projects, Autumn 2012

http://cs229.stanford.edu/projects2012.html

CS 229 Machine Learning. Final Projects, Autumn 2012. A Facebook Profile-Based TV Recommender System. Jeff David, Samir Bajaj, Cherif Jazra. [pdf]. A Flexible System for Hand Gesture Recognition. Matt Vitelli, Dominic Becker, Laza Upatising. [pdf]. A New Rival To Predator And ALIEN. Martin Raison, Botao Hu. [pdf]. A Risky Proposal: Designing a Risk Game Playing Agent. Juan Lozano, Dane Bratz. [pdf]. A Supervised Learning Method for Seismic Data Quality Control. Travis Addair. [pdf]. Association of enhanc...

3

CS 229: Machine Learning Final Projects, Autumn 2013

http://cs229.stanford.edu/projects2013.html

CS 229 Machine Learning. Final Projects, Autumn 2013. Jazz: Automatic Music Genre Detection. Tom Camenzind, Shubham Goel. [pdf]. 2D Visualization of High-Dimensional Molecular Data from Single-Cell Mass Cytometry. Yishun Dong, Diana Wan. [pdf]. 2D Visualization of Immune System Cellular Protein Data by Nonlinear Dimensionality Reduction. Andre Esteva, Anand Sampat, Amit Badlani. [pdf]. Application of Classification Algorithms to Renaissance Music Attribution. Alex Adamson. [pdf]. Etan Green. [pdf]. Astro...

4

CS 229: Machine Learning (Course schedule)

http://cs229.stanford.edu/schedule.html

Handout #2: Course Schedule. Supervised learning setup. LMS. Logistic regression. Perceptron. Exponential family. Generative learning algorithms. Gaussian discriminant analysis. Naive Bayes. Model selection and feature selection. Ensemble methods: Bagging, boosting. Evaluating and debugging learning algorithms. Bias/variance tradeoff. Union and Chernoff/Hoeffding bounds. VC dimension. Worst case (online) learning. Practical advice on how to use learning algorithms. EM Mixture of Gaussians. Late days cann...

5

CS 229: Machine Learning (Course handouts)

http://cs229.stanford.edu/materials.html

Handouts and Problem Sets. Handout #1: Course Information (HTML). Handout #2: Course Schedule (HTML). Lecture notes 1 (ps). Supervised Learning, Discriminative Algorithms. Lecture notes 2 (ps). Lecture notes 3 (ps). Lecture notes 4 (ps). Lecture notes 5 (ps). Regularization and Model Selection. Lecture notes 6 (ps). Online Learning and the Perceptron Algorithm. (optional reading). Lecture notes 7a (ps). Unsupervised Learning, k-means clustering. Lecture notes 7b (ps). Lecture notes 8 (ps). A list of last...

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机器学习之线性回归

http://houzhicheng.com/ml/2013/04/28/linear-regression-of-machine-regression.html

Apr 28, 2013. 其中 $ theta$ 称为参数或权重, $x$ 为输入或特征,$n$ 为特征个数 不包括$x 0$. 线性回归的优化目标是使cost function最小,其中cost function 用 $J( theta)$ 表示, 为. 这个函数叫做普通最小二乘函数 ordinary least squares。 其中$ theta$开始与初始猜测值,并且更新同时作用于所有参数$j= 0, n$。 这就是所谓的最小二乘法 Least Mean Squares, LMS ,也叫Widrow-Hoff法。 当训练样本多于一个时,由此引出两种方法可以处理多个样本的样本集 批梯度下降法(batch gradient descent)和随机梯度下降法或增量梯度下降法(stochastic gradient descent or incremental gradient descent). Repeat until convergence {. For i=1 to m, {. 其中$X$ 为$m-by-n 1$的矩阵,$(x {(i)}) T$ 为第$i$个训练样本:.

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机器学习之logistic回归

http://houzhicheng.com/blog/ml/2014/01/11/machine-learning-logistic_reg.html

Jan 11, 2014. 其中 $ theta$ 称为参数或权重, $x$ 为输入或特征,$n$ 为特征个数 不包括$x 0$. 对于该假设模型的解释为 对于输入$x$,$h(x)$ 为在$ theta$条件下,分类输出为1的概率,数学表示如下: $ h(x) = P(y=1. X; theta) $. Logistic回归的优化目标是使cost function最小,其中cost function 用 $J( theta)$ 表示, 为. 对比发现这和线性回归的形式一模一样,但其实这并不是相同的算法,因为不同于线性回归,logistic 回归的$h(x)$是 $ theta T x$ 的非线性函数。 Logistic 回归的假设模型在$ theta Tx$后加入了一层sigmoid函数,此时$ theta Tx$不作为直接输出而是作为决策边界,对于线性的$ theta Tx$决策边界为一条直线,直线两侧分为两类,对于非线性的$ theta Tx$,决策边界则为非线性的。 Stanford machine learning cs229. Tech blog, interest in big data.

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Resources - Yichao Xu

http://www.xuyichao.cn/resources.html

Zouxy09: a list of code and papers for CV, ML. Wu Huaiyu@CAS: all kinds of resources. Computer Vision Industry: a list by David Lowe. Advances in Computer Vision @ MIT. Computer Vision @ U Washington. Machine Learning @ Stanford. Image Understanding II @ UBC. Computational Photography [MIT Raskar]. Foundations of Variational Image Analysis @ Universität Heidelberg. Middlebury: A Database and Evaluation Methodology for Optical Flow. SimpleFlow: A Non-iterative, Sublinear Optical Flow Algorithm.

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The second first quarter | Culture Shock

https://czinamerica.wordpress.com/2014/10/18/the-second-first-quarter

Chuan-Zheng discovers America. (Standard opinion disclaimers apply.). The second first quarter. One year ago today. Drowning in a sea of coursework and ruthlessly dragged along by the high-speed train of graduate school, I wrote, if I may quote myself, that “there must be at least some element of masochism in anyone who voluntarily stays around here.” That suspicion turned out to be correct, perhaps even a mild understatement. The introductory electrical engineering class I’m helping teach this qua...

fdatamining.blogspot.com fdatamining.blogspot.com

F# and Data Mining: October 2014

http://fdatamining.blogspot.com/2014_10_01_archive.html

F# and Data Mining. Saturday, October 4, 2014. A list of references in machine learning and programming languages. Minka, A comparison of numerical optimizers for logistic regression. 160;         Estimating a Gamma distribution. 160;         Beyond Newton’s method. Steyvers, Multidimensional scaling. Von Luxburg, A tutorial of spectral clustering. Das et. al, Google news personalization: scalable online collaborative filtering. Hofmann, Probabilistic latent semantic analysis. Wickham, Tidy data. 160;&#1...

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F# and Data Mining: A list of references in machine learning and programming languages

http://fdatamining.blogspot.com/2014/10/a-list-of-references-in-machine.html

F# and Data Mining. Saturday, October 4, 2014. A list of references in machine learning and programming languages. Minka, A comparison of numerical optimizers for logistic regression. 160;         Estimating a Gamma distribution. 160;         Beyond Newton’s method. Steyvers, Multidimensional scaling. Von Luxburg, A tutorial of spectral clustering. Das et. al, Google news personalization: scalable online collaborative filtering. Hofmann, Probabilistic latent semantic analysis. Wickham, Tidy data. 160;&#1...

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[Stanford]机器学习(CS229) - CS·Poetry

http://cspoetry.com/stanford机器学习cs229.html

课程主页: cs229.stanford.edu. 课程材料下载地址 含讲义,作业题,课程笔记 cs229.stanford.edu/materials.html. 课程视频 网易公开课 : 斯坦福大学公开课 机器学习课程. Hinton的 这个coursera上貌似也有 : Introduction to Neural Networks and Machine Learning. CMU的 注意无中文 : Introduction to Machine Learning. USC的Machine Learning: An introductory course on machine learning. CMU的统计机器学习 此为上面那个的进阶课程 : Statistical Machine Learning. Comments are off this post. Graduate in ML and CG.

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CS 229: Machine Learning

The first discussion section will be held on Friday 9/26, in the NVIDIA auditorium, from 4:15 - 5:05 pm. It will cover some materials in linear algebra useful for this course. The first class will be held at 9:00 am on Monday 9/22, in the NVIDIA auditorium. We look forward to seeing you there! Data for problem set 1 can be downloaded here q1x.dat. The project guideline has been released. Please check here. The suggested projects list has been released. Please check here. Materials from the Matlab tutorial.

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CS229A - Applied Machine Learning

All future course announcements will be made at http:/ stanford.ml-class.org/. Final Project Guideline: projectGuidelines.pdf. The first class will meet on Monday September 26th, 4.15-5.30pm in Hewlett 103. We hope to see you there! What is machine learning? This class' emphasis is on Applied. How will this class work? This is an online. How can I find out more about the class? Additional information is on the Course Information. Page Common questions are also answered on the FAQ. How do I sign up?

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CS23 - complex IT solution - počítače, notebooky, servery, prodej a servis

CS23, s.r.o. 460 10 Liberec 10. Tel: 420 485 152 440. Mob: 420 602 118 599. Vyberte výrobce ze seznamu:. Řešení internetových obchodů iDeal realizuje E LINKX, a.s. Pro opuštění, šipka vlevo/vpravo. Pro pohyb mezi snímky.

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CS231n Convolutional Neural Networks for Visual Recognition

CS231n Convolutional Neural Networks for Visual Recognition. These notes accompany the Stanford CS class CS231n: Convolutional Neural Networks for Visual Recognition. Feel free to ping @karpathy. If you spot any mistakes or issues, or submit a pull request to our git repo. We encourage the use of the hypothes.is. Extension to annote comments and discuss these notes inline. Assignment #1: Image Classification, kNN, SVM, Softmax. Assignment #2: Neural Networks, ConvNets I. Python / Numpy Tutorial. Gradient...