- You want to learn machine learning or data science. You might want a job or the opportunity to get a job in machine learning or data science. Alternatively, you might.
- Required Textbook: IIR = Introduction to Information Retrieval, by C. Cambridge University Press, 2008. This book is available.
- Courses offered by the School of Engineering are listed under the subject code ENGR on the Stanford Bulletin's ExploreCourses web site. The School of Engineering.
- StatLearning Intro to Statistical Learning Stanford University. This is an introductory-level course in supervised learning, with a focus on.
- Machine learning is the study of computer algorithms that improve automatically through experience and has been central to AI research since the field's inception.
- Andrew Ng's research is in machine learning and in statistical AI algorithms for data mining, pattern recognition, and control. He is interested in the analysis of.
Analytics professionals, modelers, big data professionals who haven't had exposure to machine learning; Yep! Engineers who want to understand or learn machine. I've picked out the very best machine learning resources. If you are a true beginner and excited to get started in the field of machine learning, I hope. Podcast.ucsd.edu offers free audio recordings of UC San Diego class lectures for download onto your music player or computer.
Information Retrieval and Web Search. CS 2. 76 / LING 2. Information Retrieval and Web Search.
Course Information. Description: Basic and advanced techniques for text- based information systems: efficient text indexing; Boolean and vector space retrieval models; evaluation and interface issues; Web search including crawling, link- based algorithms, and Web metadata; text/Web clustering, classification; text mining. Lectures: 3 units, Tu/Th 4: 3. NVIDIA Auditorium (available online through SCPD)Course policies: details here. Prerequisites: CS 1. CS 1. 09, CS 1. 61.
Ideally the whole CS Major Core. This year's programming assignments will be in Java. The material here may be helpful to refresh your Java knowledge. Visit Coursera to find video chunks. To sign up, visit this signup link, note that you need to sign up with your @stanford.
It facilitates lively discussion among. Join CS2. 76 using the entry code 9. WNEZM. However, occasionally there might be changes to schedule given above.
Please check this calendar before visiting office hours. We will try to avoid last minute changes and will make a Piazza announcement if necessary.
For SCPD students, we will be available via Google hangout. Hangout link will be posted on Piazza. Notes. . 1. MG 3.
MIR 8. 2 Shakespeare plays. Lab: Merge algorithm for.
Starter code. PA1 Out. CMCoursera. Term Vocabulary and Postings Lists (IIR Ch. Notes. . 3. 6 4. 3. MIR 7. 2. Porter's stemmer (MIR). Porter stemming algorithm (Official)A skip list cookbook (Pugh 1. Fast phrase querying with combined indexes (Williams, Zobel, Bahle 2. Efficient phrase querying with an auxiliary index (Bahle, Williams, Zobel 2.
Week 2. 4/0. 5Lab: Algorithms for postings list compression. Starter code. CMCoursera. Index Compression (IIR Ch.
Notes. . 5. MG 3. Compression of inverted indexes for fast query evaluation (Scholer et al. Inverted index compression using word- aligned binary codes (Anh and Moffat 2.
Spelling correction. MG 4. 2. Techniques for automatically correcting words in text (Kukich 1. Finding approximate matches in large lexicons (Zobel and Dart 1.
Efficient Generation and Ranking of Spelling Error Corrections (Tillenius)Week 3. Guest speaker: Udi Manber(Attendance required for. Udi. 4/1. 4 Probabilistic IR: Binary Independence Model .
Week 4. 4/1. 9 Computing Scores and BM2. F. . 7). Notes. . Recent evaluation, NDCG, using clickthrough; rate queries & results .
Notes. . 3. Week 5. Systems issues in efficient retrieval and scoring. Efficient. Query Evaluation using a Two- Level Retrieval Process (Broder.
Class lab: Mapreduce with Java . Readings. IIR Ch. IIR Ch. 1. 4Machine learning in automated text categorization (Sebastiani 2. A re- examination of text categorization methods (Yang et al. A Comparison of event models for naive Bayes text classification (Mc. Callum et al. Mc. Graw- Hill, 1. 99.
Open Calais. Weka. Reuters- 2. 15. 78. Tackling the poor assumptions of Naive Bayes classifier (Rennie et al.
Machine learning in automated text categorization (Sebastiani 2. Tom Mitchell. Machine Learning. Mc. Graw- Hill, 1. Evaluating and optimizing autonomous text classification systems (Lewis 1. Trevor Hastie, Robert Tibshirani, Jerome Friedman. Elements of Statistical Learning: Data Mining, Inference, and Prediction.
Springer- Verlag, New York, 2. Support vector machines. A tutorial on support vector machines for pattern recognition (Burges 1. Using SVMs for text categorization (Dumais. Inductive learning algorithms and representations for text categorization (Dumaiset a.
A Re- examination of text categorization methods (Yang et al. Text categorization based on regularized linear classification methods (Zhang et al. Trevor Hastie, Robert Tibshirani, Jerome Friedman. Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer- Verlag,New York, 2. Reuters- 2. 15. 78. Thorsten Joachims.
Learning to Classify Text using Support Vector Machines. Week 7. 5/1. 0. Learning to rank.
A support vector method for optimizing average precision (Yue et al.
Topics include: computer maintenance and security, computing resources, Internet privacy, and copyright law. One- hour lecture/demonstration in dormitory clusters prepared and administered weekly by the Resident Computer Consultant (RCC). Not a programming course. Class will consist of video tutorials and weekly hands- on lab sections.
The time listed on AXESS is for the first week's logistical meeting only. Topics include: grep and regular expressions, ZSH, Vim and Emacs, basic and advanced GDB features, permissions, working with the file system, revision control, Unix utilities, environment customization, and using Python for shell scripts. Queen Greatest Hits 1 2 Zip.
Topics may be added, given sufficient interest. Course website: http: //cs.
Drawing on multiple sources of actual interview questions, students will learn key problem- solving strategies specific to the technical/coding interview. Students will be encouraged to synthesize information they have learned across different courses in the major. Emphasis will be on the oral and combination written- oral modes of communication common in coding interviews, but which are unfamiliar settings for problem solving for many students.
Prerequisites: CS 1. B or X. Soon we are likely to entrust management of our environment, economy, security, infrastructure, food production, healthcare, and to a large degree even our personal activities, to artificially intelligent computer systems. How will society respond as versatile robots and machine- learning systems displace an ever- expanding spectrum of blue- and white- collar workers? Will the benefits of this technological revolution be broadly distributed or accrue to a lucky few? How can we ensure that these systems respect our ethical principles when they make decisions at speeds and for rationales that exceed our ability to comprehend?
What, if any, legal rights and responsibilities should we grant them? And should we regard them merely as sophisticated tools or as a newly emerging form of life?
The goal of CS2. 2 is to equip students with the intellectual tools, ethical foundation, and psychological framework to successfully navigate the coming age of intelligent machines. Exploring well- known literary texts, digital storytelling forms and literary communities online, students work individually and in interdisciplinary teams to develop innovative projects aimed at bringing literature to life. Tasks include literary role- plays on Twitter; researching existing digital pedagogy and literary projects, games, and apps; reading and coding challenges; collaborative social events mediated by new technology. Minimal prerequisites which vary for students in CS and the humanities; please check with instructors. Same as: COMPLIT 2.
B, ENGLISH 2. 39. BCS 4. 0N. Topics include the Unix file system, shell programming, file filtering utilities, the emacs text editor, elisp programming, git internals, make, m. The seminar will emphasize topics that are not covered in other classes and that complement traditional programming skills to make students more productive programmers. Students will be expected to bring laptops to class and boot linux (using external USB storage is fine). Concepts will be made more concrete through a series of shell programming and elisp exercises. Prerequisites: some programming experience or concurrent enrollment in CS 1. B. Primary focus on developing best practices in writing Python and exploring the extensible and unique parts of Python that make it such a powerful language.
Topics include: data structures (e. We will also cover object- oriented design, the standard library, and common third- party packages (e. Time permitting, we will explore modern Python- based web frameworks and project distribution. Prerequisite: 1. 06.
B/X or equivalent. Course consists of in- class activities and programming assignments that challenge students to create functional web apps (e. Yelp, Piazza, Instagram).
In- depth introduction to machine learning in 1. In January 2. 01. Stanford University professors Trevor Hastie and Rob Tibshirani (authors of the legendary Elements of Statistical Learning textbook) taught an online course based on their newest textbook, An Introduction to Statistical Learning with Applications in R (ISLR). I found it to be an excellent course in statistical learning (also known as “machine learning”), largely due to the high quality of both the textbook and the video lectures. And as an R user, it was extremely helpful that they included R code to demonstrate most of the techniques described in the book. If you are new to machine learning (and even if you are not an R user), I highly recommend reading ISLR from cover- to- cover to gain both a theoretical and practical understanding of many important methods for regression and classification. It is available as a free PDF download from the authors’ website.
If you decide to attempt the exercises at the end of each chapter, there is a Git. Hub repository of solutions provided by students you can use to check your work.
As a supplement to the textbook, you may also want to watch the excellent course lecture videos (linked below), in which Dr. Tibshirani discuss much of the material. In case you want to browse the lecture content, I’ve also linked to the PDF slides used in the videos. Chapter 2: Statistical Learning (slides, playlist)Chapter 3: Linear Regression (slides, playlist)Chapter 4: Classification (slides, playlist)Chapter 5: Resampling Methods (slides, playlist)Chapter 6: Linear Model Selection and Regularization (slides, playlist)Chapter 7: Moving Beyond Linearity (slides, playlist)Chapter 8: Tree- Based Methods (slides, playlist)Chapter 9: Support Vector Machines (slides, playlist)Chapter 1.
Unsupervised Learning (slides, playlist)Related. To leave a comment for the author, please follow the link and comment on their blog: R - Data School. R- bloggers. com offers daily e- mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot. Boxplots, maps, animation), programming (RStudio, Sweave, La.
Te. X, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more..
Inspired by awesome- php. If you want to contribute to this list (please do), send me a pull request or contact me @josephmisiti. Also, a listed repository should be deprecated if: Repository's owner explicitly say that . It is fast, easy to install, and supports CPU and GPU computation. CCV - C- based/Cached/Core Computer Vision Library, A Modern Computer Vision Library.
VLFeat - VLFeat is an open and portable library of computer vision algorithms, which has Matlab toolbox. Speech Recognition. HTK - The Hidden Markov Model Toolkit (HTK) is a portable toolkit for building and manipulating hidden Markov models. Open. CV - Open. CV has C++, C, Python, Java and MATLAB interfaces and supports Windows, Linux, Android and Mac OS. DLib - DLib has C++ and Python interfaces for face detection and training general object detectors. EBLearn - Eblearn is an object- oriented C++ library that implements various machine learning models.
VIGRA - VIGRA is a generic cross- platform C++ computer vision and machine learning library for volumes of arbitrary dimensionality with Python bindings. A scalable C++ machine learning library. DLib - A suite of ML tools designed to be easy to imbed in other applicationsencog- cppshark. Vowpal Wabbit (VW) - A fast out- of- core learning system.
Suite of fast incremental algorithms. Shogun - The Shogun Machine Learning Toolbox. Caffe - A deep learning framework developed with cleanliness, readability, and speed in mind. Commonly used for NLP. Disrtibuted Machine learning Tool Kit (DMTK) - A distributed machine learning (parameter server) framework by Microsoft. Enables training models on large data sets across multiple machines. Current tools bundled with it include: Light.
LDA and Distributed (Multisense) Word Embedding. General purpose graph library. Warp- CTC - A fast parallel implementation of Connectionist Temporal Classification (CTC), on both CPU and GPU. CNTK - The Computational Network Toolkit (CNTK) by Microsoft Research, is a unified deep- learning toolkit that describes neural networks as a series of computational steps via a directed graph.
Deep. Detect - A machine learning API and server written in C++1. It makes state of the art machine learning easy to work with and integrate into existing applications. Fido - A highly- modular C++ machine learning library for embedded electronics and robotics. DSSTNE - A software library created by Amazon for training and deploying deep neural networks using GPUs which emphasizes speed and scale over experimental flexibility. Intel(R) DAAL - A high performance software library developed by Intel and optimized for Intel's architectures. Library provides algorithmic building blocks for all stages of data analytics and allows to process data in batch, online and distributed modes. MLDB - The Machine Learning Database is a database designed for machine learning.
Send it commands over a RESTful API to store data, explore it using SQL, then train machine learning models and expose them as APIs. Regularized Greedy Forest - Regularized greedy forest (RGF) tree ensemble learning method. MIT Information Extraction Toolkit - C, C++, and Python tools for named entity recognition and relation extraction.
CRF++ - Open source implementation of Conditional Random Fields (CRFs) for segmenting/labeling sequential data & other Natural Language Processing tasks. CRFsuite - CRFsuite is an implementation of Conditional Random Fields (CRFs) for labeling sequential data. BLLIP Parser - BLLIP Natural Language Parser (also known as the Charniak- Johnson parser)colibri- core - C++ library, command line tools, and Python binding for extracting and working with basic linguistic constructions such as n- grams and skipgrams in a quick and memory- efficient way. Unicode- aware regular- expression based tokenizer for various languages.
Supports Fo. Li. A format. C++ library for the Fo. Li. A formatfrog - Memory- based NLP suite developed for Dutch: Po.