BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/New_York BEGIN:DAYLIGHT DTSTART:20230312T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:EDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20231105T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:EST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20230626T021316Z UID:1774F9E7-4DDF-4433-B1E5-46AFDE6C9BDF DTSTART;TZID=America/New_York:20230506T133000 DTEND;TZID=America/New_York:20230623T163000 DESCRIPTION:May 6 through June 24\, 2023. Saturdays 1:30-4:30pm.\n\nRegiste r now\, last call as its less than 2 weeks away until start date.\n\nThe I EEE North Jersey Section Communications Society (ComSoc chapter) is offeri ng a course entitled "INTRODUCTION TO MACHINE LEARNING". As more and more organizations make a push for data-driven decisions\, it is important to k now how to extract value from the information available. This course will provide practical experience with these techniques so students can be prod uctive with computational approach to using modern tools for analyzing dat a.\n\nThis course is an introduction to statistical techniques for machine learning and data mining\, assuming basic background knowledge of python programming and basic math. It emphasizes mathematical methods and compute r applications related to automated learning for prediction\, classificati on\, knowledge discovery\, and forecasting in modern data science. Special emphasis will be given to the collection\, mining\, and analysis of large data sets.\n\nStatistical software (Python\, Scikit-learn) will be used t hroughout the course for the exploration of different learning algorithms and for the creation of appropriate graphics for analysis. Applications in clude recommendation systems\, predictive customer models\, text mining\, and sentiment analysis.\n\nThe IEEE North Jersey Section's Communications Society Chapter can arrange for providing IEEE Certificate of Completion ( free) and CEUs - Continuing Education Units (for a $5 charge) upon complet ion of the course. Course prices: $150 for Undergrad/Grad/Life/ComSoc memb ers\, $200 for IEEE members\, $300 for non-IEEE members\n\nCo-sponsored by : Education Committee\n\nSpeaker(s): Thomas Long\, \n\nAgenda: \nTopics: T he primary objective of this course is to provide students with an underst anding of the statistical tools and techniques used in machine learning an d data mining. The material covered includes an introduction to the concep ts of machine learning and data mining and uses an applied exploratory app roach. On the completion of the course\, students will be able to:\n\n1. D escribe the concepts of machine learning and identify examples of its use in data science\n2. Employ statistical software to collect data\,create tr aining and test sets\, and perform predictions\n3. Identify the characteri stics of massive data sets and describe the tools needed to analyze them\n 4. Create regression models for predicting outcome variables in terms of p redictors\n5. Perform classifications for data sets using nearest neighbor and probabilistic algorithms\n6. Analyze decision tree models and display them with appropriate graphics\n7. Introduce unsupervised algorithms usin g k-means clustering\n\nSubjects covered include: Python and Statistics\, Data Cleaning\, Exploratory Data Analysis\, Regression (Multiple\, Polynom ial\, Logistic)\, Classification\, k-Nearest Neighbors\, Decision Trees\, Ensemble Methods\, Bayes\, Unsupervised Learning\, k-means clustering.\n\n Technical Requirements: Students will need access to the Python programmin g language. In addition to a standard Python installation\, most programmi ng exercises will use the package Scikit-learn. Basic programming skills a nd some familiarity with the Python language are assummed.\nStudents are e xpected to be able to bring a laptop onto which most of these libraries ca n be pre-installed using python's pip install. to learning more about both Data Science and Python.\n\nThe course is intended to be subdivided into 3-hour sessions. Each lecture is further subdivided into lecture\, guided and independent project based exercises to build experience with hands-on techniques. This course will be held at FDU - Teaneck\, NJ campus. Checks should NOT be mailed to this address or online payments collected. Email t he organizer for any questions about course\, registration\, or other issu es.\n\nRoom: Room 302\, Bldg: Becton Building \, FDU Metropolitan Campus\, 960 River Road\, Teaneck\, New Jersey\, United States\, 07666 LOCATION:Room: Room 302\, Bldg: Becton Building \, FDU Metropolitan Campus\ , 960 River Road\, Teaneck\, New Jersey\, United States\, 07666 ORGANIZER:a.j.patel@ieee.org SEQUENCE:7 SUMMARY:Introduction To Machine Learning URL;VALUE=URI:https://events.vtools.ieee.org/m/355844 X-ALT-DESC:Description: <br /><p>May 6 through June 24\, 2023. Saturdays 1: 30-4:30pm.&nbsp\;&nbsp\;</p>\n<p>Register now\, last call as its less than 2 weeks away until start date.</p>\n<p>The IEEE North Jersey Section Comm unications Society (ComSoc chapter) is offering a course entitled "INTRODU CTION TO MACHINE LEARNING". As more &nbsp\;and more organizations make a p ush for data-driven decisions\, it is important to know how to extract val ue from the information available. &nbsp\;This course will provide practic al experience with these techniques so students can be productive with com putational approach to using modern tools for analyzing data.</p>\n<p>This course is an introduction to statistical techniques for machine learning and data mining\, assuming basic background knowledge of python programmin g and basic math.&nbsp\; It emphasizes mathematical methods and computer a pplications related to automated learning for prediction\, classification\ , knowledge discovery\, and forecasting in modern data science. Special em phasis will be given to the collection\, mining\, and analysis of large da ta sets.</p>\n<p>Statistical software (Python\, Scikit-learn) will be used throughout the course for the exploration of different learning algorithm s and for the creation of appropriate graphics for analysis. Applications include recommendation systems\, predictive customer models\, text mining\ , and sentiment analysis.</p>\n<p>The IEEE North Jersey Section's Communic ations Society Chapter can arrange for providing IEEE Certificate of Compl etion (free) and CEUs - Continuing Education Units (for a $5 charge) upon completion of the course.&nbsp\; Course prices: $150 for Undergrad/Grad/Li fe/ComSoc members\, $200 for IEEE members\, $300 for non-IEEE members</p>< br /><br />Agenda: <br /><p>Topics: The primary objective of this course i s to provide students with an understanding of the statistical tools and t echniques used in machine learning and data mining. The material covered i ncludes an introduction to the concepts of machine learning and data minin g and uses an applied exploratory approach. On the completion of the cours e\, students will be able to:</p>\n<p>1. Describe the concepts of machine learning and identify examples of its use in data science<br />2. Employ s tatistical software to collect data\,create training and test sets\, and p erform predictions<br />3. Identify the characteristics of massive data se ts and describe the tools needed to analyze them<br />4. Create regression models for predicting outcome variables in terms of predictors<br />5. Pe rform classifications for data sets using nearest neighbor and probabilist ic algorithms<br />6. Analyze decision tree models and display them with a ppropriate graphics<br />7. Introduce unsupervised algorithms using k-mean s clustering</p>\n<p>Subjects covered include: Python and Statistics\, Dat a Cleaning\, Exploratory Data Analysis\, Regression (Multiple\, Polynomial \, Logistic)\, Classification\, k-Nearest Neighbors\, Decision Trees\, Ens emble Methods\, Bayes\, Unsupervised Learning\, k-means clustering.</p>\n< p>Technical Requirements: Students will need access to the Python programm ing language. In addition to a standard Python installation\, most program ming exercises will use the package Scikit-learn. &nbsp\;Basic programming skills and some familiarity with the Python language are assummed.<br />S tudents are expected to be able to bring a laptop onto which most of these libraries can be pre-installed using python's pip install. &nbsp\; to lea rning more about both Data Science and Python.</p>\n<p>The course is inten ded to be subdivided into 3-hour sessions. Each lecture is further subdivi ded into lecture\, guided and independent project based exercises to build experience with hands-on techniques.&nbsp\; This course will be held at F DU - Teaneck\, NJ campus.&nbsp\; Checks should NOT be mailed to this addre ss or online payments collected.&nbsp\; Email the organizer for any questi ons about course\, registration\, or other issues.</p> END:VEVENT END:VCALENDAR