machine learning university of washington github
Intern at Point B in Seattle as a Business Intelligence developer helping the team set up an end-to-end BI system which will help the business perform self service BI and personalized data analysis. in mathematics and applied mathematics at Tsinghua University. -- Scalable code to run on any twitter dataset. jweyn@uw.edu. All incoming and current students are eligible to apply. Here is my resume (as of January 2019) You can also find me on: LinkedIn Github Google Scholar. third one shows how top Bowlers have performed over the years. The UW Department of Statistics now offers a PhD track in the area of Machine Learning and Big Data. University of Washington - Machine learning - Regression - k-fold-ridge.r. There are 4 courses in the Machine Learning Specialization provided by University of Washington via Coursera. ... Machine Learning: License. -- Created a visualization in D3 to explore the relationship between World Employment vs Internet usage in various countries. Optimizing Distributed Systems using Machine Learning phd thesis Ignacio Cano Paul G. Allen School of Computer Science & Engineering, University of Washington, 2019 Fond of data, I was motivated to take up a job in this field. -- These techniques have been implemented using 'R'. Dr. Zheng (Thomas) Tang is now a Research Scientist - Amazon One at Amazon.He was an Intelligent Video Analytics Intern at NVIDIA from 2018 to 2019. This repo contains lectures and assignments of University of Washington - Coursera. -- Captured raw data from 2010 to 2014 and cleaned it to make it visualization ready. Currently pursuing the Data Science specialization at the UW. I am broadly interested in safe and interpretable machine learning with applications in natural language processing. As a Data Science for Social Good fellow at the University of Chicago in 2015, I helped develop the Legislative Influence Detector. If nothing happens, download GitHub Desktop and try again. About me. Biography. -- Refer the documents via the link for detailed analysis of the techniques and datasets used. Online Meta-Learning Chelsea Finn, Aravind Rajeswaran, Sham Kakade, Sergey Levine International Conference on Machine Learning (ICML) 2019; arXiv:1902.08438. Preprints Course Materials My first hands on real data. -- First graph shows top Batsman performance from both the teams, second shows the team results of all the series and the I am interested in exploring ways to make Machine Learning trustworthy. This Specialization from leading researchers at the University of Washington introduces you to the exciting, high-demand field of Machine Learning. Dr. Brunton's research focuses on combining techniques in dimensionality reduction, sparse sensing, and machine learning for the data-driven discovery and control of complex dynamical systems. Use Git or checkout with SVN using the web URL. We will be covering various aspects of deep learning systems, including: basics of deep learning, programming models for expressing machine learning models, automatic differentiation, memory optimization, scheduling, distributed learning, hardware acceleration, domain specific languages, and model serving. Clone via HTTPS Clone with Git or checkout with SVN using the repository’s web address. Coursera Assignment and Project. I earned my PhD studying Machine Learning as a student of Prof. Sham M. Kakade at the University of Washington Seattle. If nothing happens, download GitHub … Techniques used: Python, pandas, numpy,scikit-learn, graphlab. Contribute to drbaguiar/Machine-Learning-Specialization-University-of-Washington- development by creating an account on GitHub. Skills include: R, SQL, Python, Javascript, D3, HTML. Tutorial on Optimal Transport in Computational Neuroscience, Neurohackademy, 2020.. If nothing happens, download GitHub Desktop and try again. download the GitHub extension for Visual Studio. Founded in 1861, the University of Washington is one of the oldest state-supported institutions of higher education on the West Coast and is one of the preeminent research universities in the world. I have studied Statistical Machine Learning. The field of data motivated me to delve further into it and I decided to pursue my Masters in Information Management with a focus on Data Science and Business Intelligence. -- Implemented different Machine Learning Algorithms on different datasets. I have explored explainability and fairness aspects of Machine Learning to make it … Prior to that, I received my B.S. Machine learning plays a role in many deployed decision systems, often in ways that are difficult or impossible to understand by human stakeholders. -- Python code to extract tweet sentiment, term frequency, top hashtags, happiest US State on live tweets. Designed BI Warehouse and ETL Architectures, Performed Data Analysis, Business reporting in Oracle BI and implemented Data security protocols to name a few responsibilities. Stanford’s Machine Learning by Andrew Ng; University of Washington’s Machine Learning Specialization; fast.ai; MITx’s Machine Learning with Python: From Linear Models to Deep Learning; StatQuest’s Machine Learning Series; Machine Learning Nanodegree Program by Udacity; Reinforcement Learning by Udacity MATH 394 (Winter 2021, UW): Probability I. STAT 516 (Autumn 2020, UW): Stochastic Modeling. Rahul Kidambi . Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations Aravind Rajeswaran, Vikash Kumar, Abhishek Gupta, John Schulman, Emanuel Todorov, Sergey Levine Assignments are done with jupyter using scikit learn. About Me. Hello! STAT 538 (Winter 2019 & Winter 2020, UW): Statistical Learning: Modeling, Prediction, and Computing. -- Visualization provides a backdrop into energy consumption in the US and the comparison between the various types of bulbs. All Solutions licensed under MIT License. Machine learning — the ability for computers to detect patterns in data and use it to make predictions — is changing our world in profound ways. -- Designed multiple filters to narrow down search by Country, Region, Income Group, Year and Population. -- Perfomed a twitter sentiment analysis on live stream data using Twitter API. Work fast with our official CLI. My wife tried their ramen and it was pretty forgettable. Explaining, in a human-understandable way, the relationship between the input and output of machine learning models is essential to the development of trustworthy machine-learning-based systems. I am an Assistant Professor of Machine Learning at MILA (Quebec Institute for Learning Algorithms)/HEC Montréal (U. Montreal’s business school). Working as a Business Intelligence Consultant, I lead my team through different projects and churning through data for 3 years. He received his Ph.D. in Electrical & Computer Engineering (ECE) from the University of Washington (UW) in 2019, and his B.S. With an experience of around 4 years in the BI space, I am one of the best you can get. Contribute to tuanavu/coursera-university-of-washington development by creating an account on GitHub. In terms of the library and packages, I only used graphlab and SFrame for Machine Learning Foundations. 2015. 6 Machine Learning Specialization Intelligent restaurant review system ©2015 Emily Fox & Carlos Guestrin All reviews for restaurant The seaweed salad was just OK, vegetable salad was just ordinary. I am also excited about optimal transport and its applications in statistics and machine learning. Research. Publications. With skills from Warehousing, ETL, BI Architecture, Reporting and Analysis, I have it all in me. CSE446: Machine Learning. Learn more. I am an acting instructor in the Department of Statistics at the University of Washington.. Use Git or checkout with SVN using the web URL. See LICENSE for further details. -- Created a application in D3.js to help users decide lighting for their homes. Supervised learning and predictive modeling: decision trees, rule induction, nearest neighbors, Bayesian methods, neural networks, support vector machines, and model ensembles. Determine when a deep neural network would be a good choice for a particular problem. I received my Ph.D. from Ecole Normale Superieure de Paris under the supervision of Alexandre d’Aspremont within the Sierra Team led by Francis Bach. -- Different tabs on the story explore different dimensions. O’Reilly Media, Inc. O’Reilly Media, Inc. Goldstein, Alex, Adam Kapelner, Justin Bleich, and Emil Pitkin. Graduate Course, University of Washington, Department of Electrical and Computer Engineering, 2019 Teaching Assistant of CSEP 546 Winter 2014: Data Mining/Machine Learning Graduate Course, University of Washington, Paul G. Allen School of Computer Science and Engineering , 2014 Learn, Implement, Repeat. Hands-on Machine Learning with Scikit-Learn and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems. Contribute to tuanavu/coursera-university-of-washington development by creating an account on GitHub. ... Code open-sourced on Github-- Exploratory data analysis has been performed on the murder statistics of 2012 in the United States.-- Different tabs on the story explore different dimensions. Bio. GitHub - Weenkus/Machine-Learning-University-of-Washington: All code used in the Machine Learning specialization from Coursera at https://www.coursera.org/specializations/machine-learning. If nothing happens, download the GitHub extension for Visual Studio and try again. Machine Learning courses. Acting Assistant Professor, Applied Mathematics, University of Washington, 2012–2014; Research Statement. PhD Student in Machine Learning. Statistics and Machine Learning. Alberta Machine Intelligence Institute - Machine Learning Algorithms: Supervised Learning Tip to Tail University of Helsinki: Object-Oriented Programming with Java, part I The Hong Kong University of Science and Technology - Python and Statistics for Financial Analysis As the technology becomes faster and more accessible, machine learning is sparking innovations big and small, from customer service chatbots to … You signed in with another tab or window. This repository contains 15 homeworks of Machine Learning course of National Taiwan University (NTU). The goal of the PhD track is to prepare students to tackle large data analysis tasks with the most advanced tools in existence today, while building a strong methodological foundation. SAMPL is an interdisciplinary machine learning research group exploring problems spanning multiple layers of the system stack including deep learning frameworks, specialized hardware for training and inference, new intermediate representations, differentiable programming, and various applications. Explain how neural networks (deep and otherwise) compare to other machine learning models. My research interests include natural language processing, semantic role labeling, multimodal learning for vision and language, and machine learning applications. Demonstrate your understanding of the material through a final project uploaded to GitHub. -- Exploratory data analysis has been performed on the murder statistics of 2012 in the United States. About this Specialization. Graduate student in Atmospheric Sciences researching applications of machine learning to ensemble weather forecasting. ... Graduate School: University of Washington. Programming Assignments for machine learning specialization courses from University of Washington through Coursera. See LICENSE for further details. My research focuses on pure exploration multi-armed bandits, recommender systems, and nonparametric estimation. Kunal Seth - Blending Data and Creativity at Adobe. My skills include Oracle BI (10G/11G), Microsoft SQL stack, Informatica, SSIS and Hive to name a few. MISC Teaching. -- Conducted a survey to analyze the current understanding of the users for home lighting. Working as a Data Insights Manager on Lightroom Ecosystem delivering the best of data driven applications and helping to nurture creativity for our customers. If nothing happens, download Xcode and try again. For all the other courses (Regression, Classification and Clustering) I have used pandas for feature enginering and … All Solutions licensed under MIT License. I am also interested in applications of machine learning that promote the social good. Oracle Certified SQL Expert and Oracle Certified DBA. I like the interior decoration and the blackboard menu on the wall. A childhood interest in computers turned into a career path when I completed my undergradute in Computer Engineering from the University of Mumbai with a Distinction Degree. My Forte. I am a post-doctoral researcher in the department of Computer Science at Cornell University. -- The visualization created in Tableau shows how Australia and England players have performed in the historic Ashes Test Series. - Offliners/NTUML2021_Hung-yi-Lee I am currently a PhD student at the University of Washington, advised by Su In Lee.I am interested in developing methods to make machine learning models more interpretable, and applying such methods to understand biological and health data. Through a series of practical case studies, you will gain applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval. • CV • Github • Scholar contact: rkidambi AT cornell DOT edu Catalog Description: Methods for designing systems that learn from data and improve with experience. I was a teaching assistant for. Graduated in June 2017 with a bag full of Dataskills. University of Washington. -- Multiple graphs designed in order to explore and visualize the safe and unsafe states with respect to Murder stats. Previously, I was a postdoctoral fellow in the NSF-TRIPODS institute ADSI (now IFDS) at the University of Washington.. Course 1: Machine Learning Foundations: A Case Study Approach This course is designed to fill this gap. I am a PhD student in the Paul G. Allen School of Computer Science and Engineering at the University of Washington, Seattle. Machine-Learning-University-of-Washington My machine learning projects about regression, classification and clustering All projects are completed using Pandas, Numpy, Scikit-learn and matplotlib. Have also implemented prediction algorithms on datasets to understand them better.