Pylearn2 is a library designed to make machine learning research easy. … Hall. 8.3 Projections 6.1 Decision Trees Credibility: Evaluating what’s been learned 12.9 WEKA Implementations Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. The easiest way to do so is to download this entire repository as a zip file. Morgan Kaufmann, 2016. 8.7 Calibrating Class Probabilities Project: The project is designed to serve as an exercise in applying one or more of the data mining techniques covered in the course to analyze real life data sets. 8.2 Discretizing Numeric Attributes Review by E. Davis (AI Journal, Vol. Ensure any written solutions are typed or easily readable by anyone. DNSC 6279 ("Data Mining"): Stochastics for Analytics I, Statistics for Analytics, or equivalent (JUD/DAD), 5.13 Further Reading and Bibliographic Notes 9.6 Graphical Models and Factor Graphs 12.7 Stacking Enterprise Miner is a proprietary commercial product and not freely available. 13.10 Further Reading and Bibliographic Notes 13.5 Text Mining Chapter2.pptx As described in Data Mining: Practical Machine Learning Tools and Techniques, 3rd Edition, you need to check different datasets, and different collections of information and combine that together to build up the real picture of what you want:There are several standard datasets that we will come back to repeatedly. Chapter11.pptx The course grade will be based on team homework assignments, a midterm and final exam, and a team project. 13.1 Applying Data Mining (Textbook 2) Ian H. Witten, Frank Eibe, Mark A. 12. and making predictions but also powers the latest advances its coverage. 4.4 Covering Algorithms: Constructing Rules of Waikato, Hamilton, New Zealand, Eibe Frank. p. cm. 31:1, March 2002). Enter the following statements on the git bash command line: $ git remote add origin https://github.com//GWU_data_mining.git, $ git remote add upstream https://github.com/jphall663/GWU_data_mining.git, $ git lfs track '*.jpg' '*.png' '*.csv' '*.sas7bdat'. DNSC 6290 ("Machine Learning") provides a follow up course to DNSC 6279 that will expand on both the theoretical and practical aspects of subjects covered in the pre-requisite course while optionally introducing new materials. ... Big Data and Machine Learning Techniques - Volume 9243, (413-421) readers who want to delve into modern probabilistic modeling and The final exam date will be made known at that time. Pattern is a web mining module for Python. Chapter5.pptx 3.5 Instance-Based Representation Keras is a higher level library that makes TensorFlow easier to use for building and training common deep learning architectures. Our book provides a highly This is a semester long project, and students have the option to work in 2-4 person teams. 12.5 Additive Regression 2.3 What’s in an Attribute? Anaconda Python Python is an approachable, general purpose programming language with excellent add on libraries for math and data analysis. The student is responsible for studying and understanding all assigned materials. 13.4 Incorporating Domain Knowledge this page) and click the 'Fork' button. Sections and chapters with new material are marked in red. An Introduction to Data Science by Jeffrey Stanton – Overview of the skills required to succeed in data science, with a focus on the tools available within R. It has sections on interacting with the Twitter API from within R, text mining, plotting, regression as well as more complicated data mining techniques… personal website). 4.7 Instance-Based Learning Regular attendance is expected, except for remote students. You may access Enterprise Miner through the SAS on Demand for Academics portal or by contacting the GWU Instructional Technology Lab. The focus will be on developing important skills in preparing data and selecting and evaluating models, though we will delve into the mathematical intuition behind each … This wiki is not the only source of information on the Weka software. Extending instance-based and linear models Appendix B: The WEKA workbench 12.1 Combining Multiple Models Chapter7.pptx 12.6 Interpretable Ensembles Techniques covered will include basic and analytical data preprocessing, regression models, decision trees, neural networks, clustering, association analysis, and basic text mining. 5.7 Predicting Probabilities 5.1 Training and Testing 4.6 Linear Models 7.4 WEKA Implementations p. cm.— Pattern recognition and machine learning: Gaussian processes in machine learning: Machine learning in automated text categorization: Machine learning: Thumbs up? approaches. preprocessing and combining output from different methods. Click here to download the online appendix on Weka, an extended version of Appendix B in the book. 9.5 Bayesian Estimation and Prediction 8.4 Sampling Techniques may include logistic and linear regression, SVMs, decision trees, neural networks, and clustering. The course aims to supply students with a useful toolbox of machine learning techniques that can be applied to real-life data. Preface 7.3 Numeric Prediction with Local Linear Models The exams are individual assignments. 13.11 WEKA Implementations 8.5 Cleansing Machine learning provides practical tools for analyzing data DNSC 6279 ("Data Mining") provides exposure to various data preprocessing, statistics, and machine learning techniques that can be used both to discover relationships in large data sets and to build predictive models. If you are struggling with an assignment or class materials, require extra time for an assignment, or simply require additional assistance, see the instructor immediately. 1.4 The Data Mining Process Techniques covered may include feature engineering, penalized regression, neural networks and deep learning, ensemble models including stacked generalization and super learner approaches, matrix factorization, model validation, and model interpretation. 131:1-2, September 2001). 9.7 Conditional Probability Models Please contact the Disability Support Services to establish eligibility and to coordinate reasonable accommodation. Students are expected to participate in these contests as individuals or in groups and to do reasonably well. 10.3 Convolutional Neural Networks The 5.8 Counting the Cost joined Ian 9.4 Hidden Variable Models It can be accessed without the need for coding through a standalone, web browser client or by installing additional coding interfaces for R and/or Python. Part 2, the WEKA machine learning workbench, is a guide into Weka, with detailed commentary to the underlying data mining method and theory. In preparing your homework assignments, please follow these guidelines: Midterm and Final Exam: A midterm exam will address content from the first half of the class and a final exam will address content from the second half of the class. Students can use a variety of software tools to perform the analysis, including standard Python, R, or SAS packages. 1.7 Data Mining and Ethics Ð (Morgan Kaufmann series in data management systems) Includes bibliographical references and index. Learn and apply key concepts of modeling, analysis and validation from Machine Learning, Data Mining and Signal Processing to analyze and extract meaning from data. Homework Assignments: You will be given several homework assignments during the semester. and his expertise in these techniques has greatly extended 9.1 Foundations Title. Data mining. MSBA Program Candidacy or instructor approval. 7.2 Extending Linear Models Data Mining: Practical Machine Learning Tools and Techniques (The Morgan Kaufmann Series in Data Management Systems) eBook: Witten, Ian … Enterprise Miner allows for the construction of complex data mining workflows without writing code. Hall, and Christopher J. Pal. 5.6 Comparing Data Mining Schemes These techniques are now running behind the scenes to discover patterns and make predictions in various applications in our daily lives. It also requires a virtual machine player which you may need to install separately. Flach (AI Journal, Vol. 13.9 Ubiquitous Data Mining Witten, Eibe January 2000. 4.9 Multi-Instance Learning 5.9 Evaluating Numeric Prediction 13.6 Web Mining Classes will be taught as workshops where groups of students will apply lecture materials to the ongoing Kaggle Advanced Regression and Digit Recognizer contests. Projects can be a group or individual assignment. Print/type your name(s) on the top right hand corner of every page or in a header of any papers submitted. 3.2 Linear Models Some copyrights are owned by other individuals and entities. 2.4 Preparing the Input MSBA Program Candidacy or instructor approval. Authors: Ian H. Witten. 6. TensorFlow is a lower-level library for performing mathematical operations. Deep learning Some code examples are copyrighted by other entities, and usually provided with an Apache Version 2 license. This course is an introduction to data (or information) mining and analysis, and covers how to analyse structured data. Homework assignments will typically require the use of software. 5.4 Other Estimates / Ian H. Witten, Frank Eibe, Mark A. Those materials or other internal information will be shared with students via Blackboard. Data mining is t he process of discovering predictive information from the analysis of large databases. Ensure any submitted computer program solutions are commented and runnable in a standard Python, R, or SAS environment. and Mark Students will learn various machine learning (or statistical learning) techniques and tools both through lectures and hands-on exercises in labs. Beyond supervised and unsupervised learning It has tools for Data Mining, Natural Language Processing, Network Analysis and Machine Learning. If you would like to take advantage of the version control capabilities of git then you need to follow these steps. (GPU support is optional but helpful for this class.) 9. XGBoost is an optimized and highly accurate library for gradient boosted regression and classification. An Introduction to Statistical Learning with R; Data Mining: Practical Machine Learning Tools and Techniques; A Visual Introduction to Machine Learning; A Course in Machine Learning; Project maintained by bait509-ubc. In case of a group assignment, all group members will receive a zero grade. II. 5.10 The Minimum Description Length Principle H2o.ai is a package of high performance functions and algorithms for preprocessing data and training statistical and machine learning models. 10.9 WEKA implementations 9.3 Clustering and Probability Density Estimation 8.9 WEKA Implementations 11.4 WEKA Implementations This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know … DNSC 6290 ("Machine Learning"): Stochastics for Analytics I, Statistics for Analytics, or equivalent (JUD/DAD), Data Mining, Learn more. Materials for GWU DNSC 6279 and DNSC 6290. Chapter9.pptx Explains how machine learning algorithms for data mining work. 3.7 Further Reading and Bibliographic Notes Different datasets tend to expose new issues and challenges, and it is interesting and instructive to have in mind a variety of problems when considering learning methods. 1. 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