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CS 229: Machine Learning (Course handouts)

CS 229: Machine Learning (Course handouts)
Lecture notes 1 (ps) (pdf) Supervised Learning, Discriminative Algorithms Lecture notes 2 (ps) (pdf) Generative Algorithms Lecture notes 3 (ps) (pdf) Support Vector Machines Lecture notes 4 (ps) (pdf) Learning Theory Lecture notes 5 (ps) (pdf) Regularization and Model Selection Lecture notes 6 (ps) (pdf) Online Learning and the Perceptron Algorithm. (optional reading) Lecture notes 7a (ps) (pdf) Unsupervised Learning, k-means clustering. Lecture notes 7b (ps) (pdf) Mixture of Gaussians Lecture notes 8 (ps) (pdf) The EM Algorithm Lecture notes 9 (ps) (pdf) Factor Analysis Lecture notes 10 (ps) (pdf) Principal Components Analysis Lecture notes 11 (ps) (pdf) Independent Components Analysis Lecture notes 12 (ps) (pdf) Reinforcement Learning and Control Supplemental notes 1 (pdf) Binary classification with +/-1 labels. Supplemental notes 2 (pdf) Boosting algorithms and weak learning. Related:  Training

Andrew Ng's Home page Andrew Ng is a co-founder of Coursera and the director of the Stanford AI Lab. In 2011 he led the development of Stanford University’s main MOOC (Massive Open Online Courses) platform and also taught an online Machine Learning class that was offered to over 100,000 students, leading to the founding of Coursera. more > Ng’s Stanford research group focuses on deep learning, which builds very large neural networks to learn from labeled and unlabeled data. more > 1000 Free Online Courses from Top Universities Take online courses from the world's top universities for free. Below, you will find 1,500 online courses from universities like Stanford, Yale, MIT, Harvard, Oxford and more. You can use this collection of online courses to learn everything you want--from history, philosophy and literature, to physics, biology, psychology, and computer science. Note: This page includes a lot of Massive Open Online Courses (MOOCs). If you want to enroll in a free version of a MOOC, please select the "Full Course, No Certificate" (edX) or "Audit" (Coursera) option. If you opt to take the course for a certificate/credential, you will be charged. Humanities & Social Sciences Art & Art History Courses Italian Renaissance - Free Online Course - Dr. Classics Courses Communication Courses Economics & Finance Courses Bookmark our collection of free online courses in Economics. Film Courses Food Courses Geography Courses Health Courses

Summary of course Machine Learning by Andrew Ng on Coursera – luckycallor This is my summary of course Machine Learning by Andrew Ng on Coursera. You can have a reference here after finishing the course. I'm glad to communicate with you and learn from each other. If you find any mistakes in the article, I would appreciate it if you pointed them out. a pdf edition 1. For m examples with n features, we can use a matrix X (with m rows and n columns) to describe the data, where row vector xi (with n+1 dimension including x0 ) represents an example, while column vector xj (with m dimension) represents a feature; or in matrix X, every element xij (row i, column j) represent the jth feature of ith example. For parameters, we use vector θ with n+1 elements to describe, where θj is correspond to xj . For labels, we use a vector y (with m elements) to represent, where element yi represent the label of ith example. And for every 1≤i≤m,xi0=0 . Hypothesis: Vector version: hθ=X∗θ Element version: hθ(xi)=n∑j=0xijθj Cost function: J(θ)=12mm∑i=1(hθ(xi)−yi)2 Gradient descent: θ=(XTX)−1XTy

Engineering Everywhere | CS229 - Machine Learning Ng's research is in the areas of machine learning and artificial intelligence. He leads the STAIR (STanford Artificial Intelligence Robot) project, whose goal is to develop a home assistant robot that can perform tasks such as tidy up a room, load/unload a dishwasher, fetch and deliver items, and prepare meals using a kitchen. Since its birth in 1956, the AI dream has been to build systems that exhibit "broad spectrum" intelligence. However, AI has since splintered into many different subfields, such as machine learning, vision, navigation, reasoning, planning, and natural language processing. To realize its vision of a home assistant robot, STAIR will unify into a single platform tools drawn from all of these AI subfields. This is in distinct contrast to the 30-year-old trend of working on fragmented AI sub-fields, so that STAIR is also a unique vehicle for driving forward research towards true, integrated AI.

Bit Twiddling Hack By Sean Eron Anderson Individually, the code snippets here are in the public domain (unless otherwise noted) — feel free to use them however you please. The aggregate collection and descriptions are © 1997-2005 Sean Eron Anderson. The code and descriptions are distributed in the hope that they will be useful, but WITHOUT ANY WARRANTY and without even the implied warranty of merchantability or fitness for a particular purpose. Contents About the operation counting methodology When totaling the number of operations for algorithms here, any C operator is counted as one operation. Compute the sign of an integer The last expression above evaluates to sign = v >> 31 for 32-bit integers. Alternatively, if you prefer the result be either -1 or +1, then use: sign = +1 | (v >> (sizeof(int) * CHAR_BIT - 1)); // if v < 0 then -1, else +1 On the other hand, if you prefer the result be either -1, 0, or +1, then use: sign = (v ! Detect if two integers have opposite signs f = v && !

Data Visualization App Using GAE Python, D3.js and Google BigQuery: Part 3 - Tuts+ Code Tutorial In the previous part of this tutorial, we saw how to get started with D3.js, and created dynamic scales and axes for our visualization graph using a sample dataset. In this part of the tutorial, we'll plot the graph using the sample dataset. To get started, clone the previous tutorial source code from GitHub. Navigate to the Google App Engine (GAE) SDK directory and start the server. Point your browser to and you should be able to see the X and Y axes that we created in the previous tutorial. Before getting started, create a new template called displayChart_3.html which will be the same as displayChart.html. From our sample dataset, we have a number of count to be plotted across a set of corresponding year. We'll represent each of the data points as circles in our visualization graph. First, we'll use d3.selectAll to select circles inside the visualization element. Next, we'll bind our dataset to the circles selection. Save changes and refresh your page.

1.4. Support Vector Machines — scikit-learn 0.17 documentation The support vector machines in scikit-learn support both dense (numpy.ndarray and convertible to that by numpy.asarray) and sparse (any scipy.sparse) sample vectors as input. However, to use an SVM to make predictions for sparse data, it must have been fit on such data. For optimal performance, use C-ordered numpy.ndarray (dense) or scipy.sparse.csr_matrix (sparse) with dtype=float64. 1.4.1. SVC, NuSVC and LinearSVC are classes capable of performing multi-class classification on a dataset. SVC and NuSVC are similar methods, but accept slightly different sets of parameters and have different mathematical formulations (see section Mathematical formulation). As other classifiers, SVC, NuSVC and LinearSVC take as input two arrays: an array X of size [n_samples,n_features] holding the training samples, and an array y of class labels (strings or integers), size [n_samples]: After being fitted, the model can then be used to predict new values: >>> clf.predict([[2., 2.]])array([1]) and . by

An AI That Can Mimic Any Artist · cat /var/log/life 19 Dec 2015 Take a look at the following two pictures. One was painted by contemporary artist Leonid Afremov, and the other was painted by an algorithm mimicking his style. The first image is the Afremov’s Rain Princess, and the second image is an imitation. What’s fascinating is that a computer algorithm automatically “painted” the imitation, given only a photograph of the dome and the image of Rain Princess as input. The algorithm used to produce the above image is described in full in a paper titled A Neural Algorithm of Artistic Style. In fine art, especially painting, humans have mastered the skill to create unique visual experiences through composing a complex interplay between the content and style of an image. The algorithm is based on one key insight — neural networks trained to perform object detection end up learning to separate content from style. At a high level, the general technique is pretty straightforward. The implementation works pretty well on still images.