(iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control), text...
This course proves that a skilled human with a whiteboard can still beat the bells and whistles of more expensively produced trainings. If you know little or nothing about Machine Learning, it will give you a solid foundation. 10 years ago ...
相信经常混Machine Learning各大学习圣地的朋友们多少有听说过斯坦福大学的CS229机器学习课程(Stanford's CS 229 Machine Learning course)的呢,今天在GitHub上看到一个项目,作者Afshine Amidi (Ecole Centrale Paris, MIT)总结了自己学习该课程的重要知识点,然后每个模块输出一张囊括重点的图表(有点像我们期末备考时候...
These are some programming exercise of Stanford Machine Learning Online Course. The algorithms were coded in python or matlab including: 1.Anomaly Detection and Recommender Systems 2.Decision Trees&Boosting 3.HMM 4.K-Means Clustering and PCA 5.Linear Regression 6.Logistic Regression (matlab/octave)...
Andrew Ng的Machine Learning课程,在网易公开课上有中文版视频http://v.163.com/special/opencourse/machinelearning.html,六维上也有资源可以下载。 引言 machine learning 定义1:Field of study that gives computers the ability to learn without being explicitly programmed. ...
Stanford-Machine-Learning-Course St**凝视上传168.46 MB文件格式zip machine learning course programming exercise (0)踩踩(0) 所需:1积分 GSYVideoPlayer 2025-02-09 11:01:12 积分:1 android_open_source 2025-02-09 11:00:29 积分:1 android_vip_media_codec2...
本栏目来源于Andrew NG老师讲解的Machine Learning课程,主要介绍大规模机器学习以及其应用。包括随机梯度下降法、维批量梯度下降法、梯度下降法的收敛、在线学习、map reduce以及应用实例:photo OCR。课程地址为:https://www.coursera.org/course/ml (一)大规模机器学习 ...
Free Course Trial – Machine Learning Course from Stanford University It is a fact that the progress made using machine learning in the past few decades have successfully provided solutions to many of the persistent real-world problems. In this course, you will get an overview of the area as ...
MatLab, Octave(free),R 概率统计:Stat 116 线性代数: math 113, cs 205 跨学科: 生物,统计, 计算机 online resources:http://cs229.stanford.edu https://see.stanford.edu/Course/CS229/47 homework, handout, lecture notes, math and equations ...
Networks are a fundamental tool for modeling complex social, technological, and biological systems. Coupled with the emergence of online social networks and large-scale data availability in biological sciences, this course focuses on the analysis of massive networks which provide several computational, al...