Uc berkeley machine learning. Mar 22, 2024 · Michael Mahoney.

0 hours of lecture per week Spring: 3. Program Website. Information Economics. The study, “A generalizable and accessible approach to machine learning with global September 27, 2021, 2:00pm. S. You progress through the curriculum by identifying critical topics in these areas, and treat them with a combination of theory, practical considerations and various contemporary case-studies. For more information see: Energy and Multiphase Transport Laboratory. 0-9. Since then, case backlog is down to 24 hours, and the time saved has allowed analysts to focus more deeply on specific cases. You reinforce your classroom learning with Computational biology focuses on the application of computational techniques to problems in molecular biology, genomics, and biophysics. Dec 4, 2019 · So last spring, the United Nations Office for the Coordination of Humanitarian Affairs (UN OCHA) and Microsoft AI partnered with UC Berkeley Discovery students to develop a machine learning – artificial intelligence algorithm that makes tagging data faster and more efficient. The next screen will show a drop-down list of all the SPAs you have permission to acc Aug 18, 2004 · Honorary Professor, Peking University, 2018-present. Applying techniques developed for static datasets to real world problems requires grappling with the Aug 22, 2022 · Cloud at California is UC Berkeley’s Largest Cloud Computing Organization and provides UC Berkeley students with opportunities to learn and apply cloud technologies. Foreign Member of the Royal Society. The efforts have been greatly supported and intensively verified by the facilities including autonomous Use various techniques and tools to collect and create vast, structured and unstructured data sets, extract valuable insights and make data-driven decisions. Using tools adapted from computer science, mathematics, statistics, physics, chemistry, and other quantitative disciplines, computational biologists address a wide variety of problems ranging from analysis of Admission Requirements. Work in Artificial Intelligence in the EECS department at Berkeley involves foundational research in core areas of deep learning, knowledge representation, reasoning, learning, planning, decision-making, vision, robotics, speech, and natural language processing. Collect, clean and preprocess data. Machine learning techniques leverage data mining to identify historic trends and inform future models. Jul 18, 2018 · Synthetic biology, like artificial intelligence (AI) machine learning, is a relatively modern field that applies emerging technologies to achieve innovation. To find out more, check out our We developed and evaluated five models to predict turnover: logistic regression, random forest, gradient boosted trees, linear support vector machines, and non-linear support vector machines. Dec 9, 2016 · Overview. Members develop hands-on skills in cloud infrastructure, data analytics, machine learning, and more through workshops and consulting projects. Hardcover and Kindle/eTextbook versions are also available. He received the PhD degree in Computer Science from Stanford University in 1985 following which he Nov 4, 2019 · Machine Learning Algorithms Help Predict Traffic Headaches. In my research, I focus on algorithms that can enable autonomous agents to acquire complex behaviors through learning, especially general-purpose methods that could enable any autonomous system to learn to solve any task. 6. The course will bridge theoretical foundations with applied data analytics by using examples and real datasets from domains such as e-commerce, social AI+Science is a core group of faculty in EECS focused on the intersection of AI+SCIENCE. In this introductory course, you learn the basic concepts of different machine-learning algorithms, answering such questions as when to use an algorithm, how to use it and what to pay attention to when using it. , "+mycalnetid"), then enter your passphrase. Internally or externally, industry clients or research groups we provide many avenues to actively engage with the field. Chick Professor in the Department of Electrical Engineering and Computer Science at the University of California at Berkeley, where he also holds appointments in vision science, cognitive science and Bioengineering. Our top-ranked programs attract stellar students and professors from around the world, who pioneer the frontiers of information science and technology with broad impact on society. Member, National Academy of Sciences. For more information see ml. The course will be project-based with an emphasis on how production systems are used at leading technology-focused companies and organizations Jan 13, 2023 · First, I explore new approaches to music creation technology with machine learning, focusing on two musical settings: beat-making and orchestration. Pieter Abbeel is Professor and Director of the Robot Learning Lab at UC Berkeley [2008- ], Co-Founder of covariant. I am in the Department of Statistics at UC Berkeley; I am also at the International Computer Science Institute (ICSI, where I am Vice President and Director of the Big Data Group ) and the Lawrence Berkeley National Laboratory (LBNL, where I am the Group Lead for the Machine Learning and Analytics Group ); I am also in the The Center for Targeted Machine Learning and Causal Inference, at UC Berkeley is an interdisciplinary research center for advancing, implementing and disseminating methodology to address problems arising in public health and clinical medicine. If you need further assistance troubleshooting technical problems (pop Statistical machine learning merges statistics with the computational sciences---computer science, systems science and optimization. View online. Aug 3, 2021 · EECS Department University of California, Berkeley Technical Report No. 0-5. A lo largo de seis meses obtienes conocimientos básicos y avanzados de ML/IA Build an AI-powered application from the ground up in our Deep Learning Course. This website provides information to users on general problems and training materials to better understand how to use and get the most out of the UC Learning Center. Request more info Complete a Rigorous, Holistic Curriculum The multidisciplinary online data science Dec 21, 2021 · Machine learning uses tools from a variety of mathematical fields. Combine your skills in statistics, programming, Machine Learning, domain expertise and data visualization. His research focuses on developing machine learning (ML) methods to solve real-world problems in precision medicine. For more information please see the Berkeley Artificial Intelligence Provides a theoretical and practical introduction to modern techniques in applied machine learning. You use Apache Spark—an open-source cluster computing framework that is garnering significant attention in the Short bio. D in Information Science. edX, part of 2U Inc. Berkeley Lab's research into machine learning builds on its foundational work in mathematics Machine Learning With TensorFlow. The Center brings the rigor and power of statistical theory together with advances in machine learning CS 289A. 0 (B) on a 4. I am also a Principal Investigator in the Delphi group. Sep 13, 2023 · Berkeley Artificial Intelligence Research Lab (BAIR) | The BAIR Lab brings together UC Berkeley researchers across the areas of computer vision, machine learning, natural language processing, planning, control, and robotics. Professor Carey is widely recognized for his research on near-interface micro- and nanoscale thermophysics and transport in liquid Overview. COMPSCI X433. These programs are offered through the UC Berkeley Extension in collaboration with edX. 0-15. The Department of Electrical Engineering and Computer Sciences (EECS) offers two graduate programs in Computer Science: the Master of Science (MS), and the Doctor of Philosophy (PhD). However, if you want a rewarding career in Introduction to Machine Learning Using Python. Where: Soda 405 (and on zoom with with link posted on Slack). 2% from 2021 to 2028. Explore the concise and expressive use of TensorFlow advanced package for Python that features many functions and methods for data mining El Certificado Profesional en Machine Learning e Inteligencia Artificial de la Universidad de Berkeley (calificada como la universidad #1 del mundo por U. berkeley. Contact Info. An adversarial attack might entail presenting a machine-learning model with inaccurate or misrepresentative data as it is training, or introducing maliciously The UC Learning Center (UCLC) is the University's system-wide learning management system (LMS) for employees. Berkeley, CA 94720. Discover the flexibility of the powerful TensorFlow package when dealing with heavy financial, mathematical, engineering or scientific problems. “Adding HXL tags is a time-consuming and repetitive task," said Jun 26, 2020 · The basic concept of machine learning in data science involves using statistical learning and optimization methods that let computers analyze datasets and identify patterns ( view a visual of machine learning via R2D3 open_in_new ). Chaire d'Excellence, Fondation Sciences Mathématiques de Paris, 2012. These models were selected due to their efficiency with binary classification tasks. Nov 22, 2022 · As part of the CIRCLES consortium, UC Berkeley researchers have taken the lead in developing the machine learning algorithms that govern how fast AI-powered vehicles should go. Cover a breadth of topics in artificial intelligence, machine learning and deep learning. The efforts have been greatly supported and intensively verified by the facilities including autonomous Research Description. Cybersecurity researchers refer to this risk as “adversarial machine learning,” as AI systems can be deceived (by attackers or “adversaries”) into making incorrect assessments. Textbooks. 6123 Etcheverry Hall. A student-run organization based at the University of California, Berkeley dedicated to building and fostering a vibrant machine learning community on the University campus and beyond. Formative Assessment in Virtual Learning Environments. 35 billion, of which North America accounted for over a 40% share of revenue in 2020. Ali Rebaie, Data Anthropologist Social scientists and policymakers increasingly use large quantities of data to make decisions and test theories. Join thousands from UC Berkeley , University of Washington , and all over the world and learn best practices for building AI-powered products from scratch with deep Undergraduates with the appropriate background and motivation are encouraged to enroll but must contact Associate Director of Student Affairs Catherine Cronquist Browning for enrollment permissions. Aug 6, 2023 · Machine Learning and Data Science Research. Introduction to Machine Learning. The Graduate Certificate in Applied Data Science introduces the tools, methods, and conceptual approaches used to support modern data analysis and decision-making in professional and applied research settings. 0 hours of lecture per week Fall: 3. We considered the logistic regression model as our baseline model and Machine Learning at Scale This course builds on and goes beyond the collect-and-analyze phase of big data by focusing on how machine learning algorithms can be rewritten and extended to scale to work on petabytes of data, both structured and unstructured, to generate sophisticated models used for real-time predictions. To sign in directly as a SPA, enter the SPA name, " + ", and your CalNet ID Final: All of the above, and in addition: Machine Learning: Kernels, Clustering, Decision Trees, Neural Networks For the Fall 2011 and Spring 2011 exams, there is one midterm instead of two. About edX. Python allows its users to create products that parse, reduce, simplify and categorize data, and then extract actionable intelligence from that data. Machine learning prerequisites are introduced including local and global optimization, various statistical and clustering models, and early meta-heuristic methods such as genetic Course Catalog Description section closed. News & World Report) se elabora en colaboración con el College of Engineering y Haas School of Business. Students in the PhD in Computational Precision Health will develop foundational competency in the computational and mathematical sciences (e. The minimum graduate admission requirements are: A bachelor’s degree or recognized equivalent from an accredited institution; A satisfactory scholastic average, usually a minimum grade-point average (GPA) of 3. Machine learning is a promising tool for processing complex information, but it remains an unreliable tool for control and decision making. Our mission is two-fold: 1) to leverage scientific insight to develop new machine learning methods, and. edX delivers boot camps through an immersive learning Jun 28, 2019 · The course offers an introduction to machine learning with R-programming that includes real-world datasets to let one solve problems for variety of industries. About: This course will cover two areas of deep learning in which labeled data is not required: Deep Generative Models and Self-supervised Learning. Machine Learning Systems. (510) 642-7177. From 2007-2011, I did my Ph. November 4, 2019. When creating data sets to be fed to algorithms to prevent human About. Sep 27, 2023 · Machine Learning and Artificial Intelligence. Units: 1-4. For example, political campaigns use surveys, marketing data, and previous voting history to optimally target get out the vote drives. Gonzalez. BAIR includes over 50 faculty and more than 300 graduate students and postdoctoral researchers pursuing research on fundamental Understand the ethical and legal requirements of data privacy and security. No theory instruction will be provided. All of the online tools you need to succeed are hosted in one place: the virtual campus. Skill Sets. At a Glance. Primary Responsibilities. in Statistics at Stanford University, with Jonathan Taylor as my thesis advisor. × COVID-19 STATEMENT: While this virus is impacting everyone differently, this online program is continuing as planned. UCB/EECS-2021-169 August 3, 2021. She has studied diverse security and privacy issues in computer systems and networks, including areas ranging from software security, networking security, database security Research Expertise and Interest. To sign in to a Special Purpose Account (SPA) via a list, add a "+" to your CalNet ID (e. It exposes students to the challenges of working with data (e. Ahmed Alaa is an Assistant Professor of Computational Precision Health at the University of California, Berkeley and the University of California, San Francisco. Sep 21, 2022 · A new UC Berkeley institute will bring together top machine learning and chemistry researchers to make this vision a reality, and a Bay Area foundation is providing a substantial gift to launch and enable this work at UC Berkeley over the next five years. I find that creative tools can benefit from incorporating machine learning if we introduce models in specific contexts motivated by well-defined musical goals. You've trained your first (or 100th) model, and you're ready to take your skills to the next level. Conceptually, the course is divided into two parts. The midterm covers all topics listed for Midterm 1, and includes Probability and Bayes' Nets. Our research covers full-stack autonomous driving, including the onboard modules such as perception, prediction, planning and control, as well as key offline components such as simulation/test, and automatic construction of HD maps and data. This document is an attempt to provide a summary of the mathematical background needed for an introductory class in machine learning, which at UC Berkeley is known as CS 189/289A. Short bio. The Multifaceted Complexity of Machine Learning , IMSI, Chicago, April 12-16, 2021. These algorithms, also called “speed planners” and “controllers,” use information about overall traffic conditions and the vehicle’s immediate surroundings to Jul 20, 2021 · Now, a team based at UC Berkeley has devised a machine learning system to tap the problem-solving potential of satellite imaging, using low-cost, easy-to-use technology that could bring access and analytical power to researchers and governments worldwide. Academic research has already shown the immense value of using satellite images with machine learning (SIML) to better understand forest cover, land use, poverty rates, and population density, among other outcomes. Formats: Fall: 2. PhD Program The PhD in Computational Precision Health leverages and bridges the complementary expertise and incredible resources of UC Berkeley and UCSF to create an unparalleled and truly unique learning environment. Governments deploy predictive algorithms in an attempt to optimize public policy processes and Machine Learning Systems Engineering The Machine Learning Systems Engineering course provides learners hands-on data management and systems engineering experience using containers, cloud, and Kubernetes ecosystems based on current industry practice. Here, you can donate and find datasets used by millions of people all around the world! View Datasets Contribute a Dataset. CS294_3731. Admissions: Please apply directly to the UC Berkeley EECS department. Berkeley, CA 94720-1740. , works with respected universities and organizations to deliver innovative, skills-based training to a community of over 45 million learners around the world to support them at every stage of their lives and careers. 2) to develop and leverage new machine learning methods to advance science. Her research interest lies in deep learning and security. NOTE: This course is cross-listed as Education 290A. Following IEOR 142A/242A, this course further introduces students to essential methodologies and recent trends in machine learning and data analytics. 0 hours of lecture per week Mar 7, 2024 · Using the TRACK-TBI Pilot Study dataset and machine learning capabilities developed by data scientists in Berkeley Lab’s Computing Sciences Area, the team analyzed hundreds of simulations on the Cori supercomputer at the National Energy Research Scientific Computing Center (NERSC) and found that 19 types of outcomes can be predicted from Welcome to the Department of Electrical Engineering and Computer Sciences at UC Berkeley. Instructor: Joseph E. Code up machine learning algorithms on single machines and on clusters of machines / Amazon AWS May 9, 2019 · Machine learning was introduced as a key part of the pipeline in 2017, automating the IP addresses and cell phone information of victims and predators. We will study three specific approaches for defending machine learning: generative models, checking internal consistency, and making improvements to adversarial training. Spring 2020. , machine learning, AI, causal and Aug 12, 2021 · It's an ideal time to launch your career in ML/AI engineering. By: Laurel Kellner. Berkeley Lab researcher Sherry Li (Credit: Roy Kaltschmidt/Berkeley Lab) Urban traffic roughly follows a periodic pattern associated with the typical “9 to 5” work schedule. Current Classes Taught. [email protected] 102 South Hall #4600. Both textbooks for this class are available free online. , " +mycalnetid "), then enter your passphrase. Mar 22, 2024 · Michael Mahoney. Select the SPA you wish to sign in as. Dr. Dec 5, 2023 · Current schemes fail badly in the presence of an attacker who is trying to fool or manipulate the model, so there is a need for better defenses. This course teaches the underlying principles required to develop scalable machine learning pipelines for structured and unstructured data at the petabyte scale. 7. This workshop introduces students to scikit-learn, the popular machine learning library in Python, as well as the auto-ML library built on top of scikit-learn, TPOT. energy storage, computational modeling, machine learning. Mathematics of Deep Learning, INI Program, July 1 - December 17, 2021. In this paper we present MLlib, Spark’s open- source distributed machine learning library. The overarching goal of his research is to develop ML models Targeted machine learning with causal and statistical inference Activities will include courses in machine learning, targeted learning, statistical programming, and big data computing, as well as workshops led by the Berkeley Data Science Institute, Statistical Computing Facility, and Berkeley Research Computing. Catalog Description: Topics will vary from semester to semester. Iliev, Lead Instructor, UC Berkeley Extension; Professor and Academic Head at SRH Berlin University of Applied Sciences. Underlying our success are a strong tradition of collaboration, close ties Consulting Drop-In Hours: Thu 3pm-5pm Consulting Areas: Python, SQL, Data Science, Machine Learning, Natural Language Processing, Text Analysis, Git or Github Quick-tip: the fastest way to speak to a consultant is to first submit a request and then The Simons Institute for the Theory of Computing is the world's leading venue for collaborative research in theoretical computer science. The next screen will show a drop-down list of all the SPAs you have permission to access. The Berkeley Artificial Intelligence Research (BAIR) Lab brings together UC Berkeley researchers across the areas of computer vision, machine learning, natural language processing, planning, control, and robotics. The WASC-accredited program blends a multidisciplinary curriculum, experienced faculty from UC Berkeley and top data-driven companies, an accomplished network of peers, and the flexibility of online learning. However, when an accident happens, traffic patterns are disrupted. How to Sign In as a SPA. ) Dawn Song is a Professor in the Department of Electrical Engineering and Computer Science at UC Berkeley. Terms offered: Fall 2024, Spring 2024, Fall 2023 An introduction to mathematical optimization and statistics and "non-algorithmic" computation using machine learning. Machine Learning at Berkeley empowers passionate students to solve real world data-driven problems through collaboration with companies and internal research. Access your weekly Zoom classes, where you will engage in meaningful Learn from UC Berkeley's globally recognized faculty, and gain a verified digital certificate of completion from UC Berkeley Executive Education Program Topics This program introduces learners to the fundamental applications of automation and machine learning, while also allowing them to explore the current capabilities and potential of Krste Asanović. If you want to brush up on prerequisite material, Stanford's machine learning class provides nice reviews of linear algebra and probability theory. Grandview Research reports that the global AI market is valued at $62. g. See Computer Science Division announcements. . Students are expected to have a solid foundation in calculus COMPSCI X419. 0 scale; and. ai [2017- ], Co-Founder of Gradescope [2014- ], Advisor to OpenAI, Founding Faculty Partner AI@TheHouse, Advisor to many AI/Robotics start-ups. The School of Information is UC Berkeley’s newest professional school. Learn why the open-source programming language Python has been extensively adopted by the machine-learning community and industry. MLlib provides efficient functionality for a wide range of learning settings and includes several underlying May 30, 2024 · Across social protection programs in Togo, Afghanistan, and Bangladesh, the studies in this dissertation show that targeting methods based on machine learning and digital data sources identify poor households more accurately than methods based on categorical eligibility criteria like geography or occupation, but typically less accurately than Welcome to the UC Irvine Machine Learning Repository. University of California, Berkeley. Professor Emeritus, Professor in the Graduate School 579B Soda Hall, 510-642-6506; krste@berkeley. CS 294-162. According to IBM, machine learning is a type of artificial intelligence (AI) that can improve how software systems process and categorize data. Covers key concepts in supervised and unsupervised machine learning, including the design of machine learning experiments, algorithms for prediction and inference, optimization, and evaluation. He works in machine learning and robotics. D. Students will learn functional, procedural, and UC Berkeley’s Laboratory for Automation Science and Engineering (AUTOLAB), directed by Professor Ken Goldberg, is a center for research in robotics and automation with 20 graduate and undergraduate students pursuing projects in Cloud Robotics, Deep Reinforcement Learning, Learning from Demonstrations, Computer Assisted Surgery, Automated To sign in to a Special Purpose Account (SPA) via a list, add a " + " to your CalNet ID (e. The term itself describes the process — ML algorithms imitate human learning and gradually improve over time as they take in larger data sets. 2. Mathematical Foundations of Machine Learning , Oberwolfach, March 21-27, 2021. Recent advances in generative models have made it possible to realistically model high-dimensional raw data such as natural images, audio waveforms and text corpora. The Computational and Experimental Design of Emerging materials Research group (CEDER) is a part of the Department of Materials Science and Engineering at UC Berkeley and the Materials Science Division at Lawrence Berkeley National Laboratory. , asking a good question, inference and causality, decision-making) as well as to the new tools and techniques for data Machine Learning Systems (Spring 2022) When: Mondays from 1:00 to 4:00. Developing novel, robust, and interpretable AI and learning methods; applying and adapting advances in AI to the complexity of science; enabling the deployment of AI applications at large computing scales. 41837219. The online master’s in data science combines advanced technology and in-person experiences to ensure you benefit from the full I School experience. Research Area (s) Data Science. The first covers Apr 25, 2024 · In 2021 three UC Berkeley Library/Research IT researchers explored the big data landscape at UC Berkeley. Much of the agenda in statistical machine learning is driven by applied problems in science and technology, where data streams are increasingly large-scale, dynamical and heterogeneous, and where mathematical and algorithmic creativity are required to bring The online master’s program brings UC Berkeley to students, wherever they are. From 2011-2022, I was a faculty member in Statistics and Machine Learning at Carnegie Mellon University. Machine Learning at Berkeley fosters an environment for passionate students to explore ML through projects, events, and education. Member, National Academy of Engineering. Workshop on the Theory of Overparameterized Machine Learning, April 20-21, 2021. Apache Spark is a popular open-source platform for large-scale data processing that is well-suited for iterative machine learning tasks. MLlib provides efficient functionality for a wide range of learning settings and includes several underlying I am an Associate professor in the Department of Electrical Engineering and Computer Sciences at UC Berkeley. Research Description. SIML has vast potential to improve the quality and frequency of information at decision-makers’ disposal, particularly in low Start by watching the video “ Latest Engineering Trends for Artificial Intelligence and Machine Learning ,” where you will meet your instructors for this seminar series: Alexander I. edu. Catalog Description: This course provides an introduction to theoretical foundations, algorithms, and methodologies for machine learning, emphasizing the role of probability and optimization and exploring a variety of real-world applications. The I School also offers a master's in Information Management and Systems (MIMS), a master's in Information and Cybersecurity (MICS), and a Ph. Furthermore, expand at a compound annual growth rate of 40. Located in the center of campus, the I School is a graduate research and education community committed to expanding access to information and to improving its usability, reliability, and credibility while preserving security and privacy. Enough undergraduate training to do graduate work in your chosen field. edu Research Interests: Computer Architecture & Engineering (ARC); Integrated Circuits (INC); Operating Systems & Networking (OSNT); Design, Modeling and Analysis (DMA) Technologies driven by machine learning (ML) and artificial intelligence (AI) have transformed industries and everyday life — from facial and voice recognition software to intelligent robotics for manufacturing, life-saving medical diagnostics, self-driving vehicles, and much more. Data plays a critical role in all areas of IEOR, from theoretical developments in optimization and stochastics to applications in automation, logistics, health care, energy, finance, and other areas. (Currently offered as Info C260F. vpcarey@berkeley. Machine Learning at Berkeley. UC Berkeley's Robot Learning Lab, directed by Professor Pieter Abbeel, is a center for research in robotics and machine learning. Students will gain hands-on experience in Apache Hadoop and Apache Spark. "Based on interviews with big data researchers at UC Berkeley as part of an Ithaka S+R project, [their] local report provides insights on researcher practices and challenges in six thematic areas: data collection & processing; analysis: methods, tools, infrastructure; research outputs Apache Spark is a popular open-source platform for large-scale data processing that is well-suited for iterative machine learning tasks. Machine learning plays an important role in big data analytics. We currently maintain 667 datasets as a service to the machine learning community. (Machine) Learning What Policymakers Value, ACM conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO '21) Author (s) Joshua Blumenstock. The Professional Certificate in Machine Learning and Artificial Intelligence from UC Berkeley (ranked the #1 university in the world by Forbes magazine) is built in collaboration with the College Sep 21, 2022 · A new UC Berkeley institute will bring together top machine learning and chemistry researchers to make this vision a reality, and a Bay Area foundation is providing a substantial gift to launch and enable this work at UC Berkeley over the next five years. . A lot of our research is driven by trying to build ever more intelligent systems, which has us pushing the frontiers of deep reinforcement learning, deep imitation learning, deep unsupervised learning, transfer learning, meta-learning, and learning to learn Jitendra Malik is Arthur J. Distinguished Visiting Professor, Tsinghua University, 2017-2019. Dec 10, 2021 · Bjorekgren, D, Knight, S, and Blumenstock, JE (2021). Now scientists at Lawrence Berkeley National Laboratory (Berkeley Lab) in California have merged the two fields by creating a machine learning algorithm for synthetic biology called ART The Online Learning Experience. The focus will be on scikit-learn syntax and available tools to apply machine learning algorithms to datasets. I am a Professor in the Department of Statistics at UC Berkeley. Other suggestions for review material appear in this Piazza post. of vb rz yj ue hv ak ip bg ww