BEGIN:VCALENDAR
VERSION:2.0
PRODID:icalendar-ruby
CALSCALE:GREGORIAN
X-WR-CALNAME:Mathematics of Big Data & Machine Learning
X-WR-TIMEZONE:Eastern Time (US & Canada)
BEGIN:VEVENT
DTSTAMP:20260906T153127Z
UID:tag:localist.com\,2008:EventInstance_41402104296818
DTSTART:20230110T150000Z
DTEND:20230110T165500Z
DESCRIPTION:Enrollment: Limited: Advance sign-up required Limited to 35 par
 ticipants\n\nAttendance: Participants must attend all sessions\n\nPrereq: 
 Matrix Mathematics\n\nBig Data describes a new era in the digital age wher
 e the volume\, velocity\, and variety of data created across a wide range 
 of fields is increasing at a rate well beyond our ability to analyze the d
 ata.  Machine Learning has emerged as a powerful tool for transforming thi
 s data into usable information.  Many technologies (e.g.\, spreadsheets\, 
 databases\, graphs\, matrices\, deep neural networks\, ...) have been deve
 loped to address these challenges.  The common theme amongst these technol
 ogies is the need to store and operate on data as tabular collections inst
 ead of as individual data elements.  This class describes the common mathe
 matical foundation of these tabular collections (associative arrays) that 
 apply across a wide range of applications and technologies.  Associative a
 rrays unify and simplify Big Data and Machine Learning.  Understanding the
 se mathematical foundations allows the student to see past the differences
  that lie on the surface of Big Data and Machine Learning applications and
  technologies and leverage their core mathematical similarities to solve t
 he hardest Big Data and Machine Learning challenges.\n\nThis interactive c
 ourse will involve significant interactive student participation and a sma
 ll amount of homework.   Those students who fully participate and complete
  the homework will receive a certificate of completion.\n\nThe MIT Press b
 ook "Mathematics of Big Data" that will be used throughout the course will
  be provided.\n\nE-mail the instructor to sign up.\n\nInstructors:\n\nHayd
 en Jananthan - Research Scientist MIT Supercomputing Center - hayden.janan
 than@ll.mit.edu\n\nJeremy Kepner - Fellow & Head MIT Supercomputing Center
  - kepner@ll.mit.edu\n\nSignup Deadline: Dec 14\n\nDates:\n\nJan 10 Tue 10
 :00AM-11:55AM Virtual Course Intro and Chapter 1\n\nJan 13 Fri 05:00PM-06:
 00PM Virtual Chapters 2 & 4 Team Prep\n\nJan 17 Tue 10:00AM-11:55AM Virtua
 l Chapters 2 & 4\n\nJan 23 Mon 05:00PM-06:00PM Virtual Chapters 5 & 6 Team
  Prep\n\nJan 24 Tue 10:00AM-11:55AM Virtual Chapters 5 & 6\n\nJan 30 Mon 0
 5:00PM-06:00PM Virtual Chapters 7 & 8 Team Prep\n\nJan 31 Tue 10:00AM-11:5
 5AM Virtual Chapters 7 & 8
LOCATION:
SUMMARY:Mathematics of Big Data & Machine Learning
URL;VALUE=URI:https://calendar.mit.edu/event/mathematics_of_big_data_machin
 e_learning_2023
CATEGORIES:Conferences/Seminars/Lectures
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260906T153127Z
UID:tag:localist.com\,2008:EventInstance_41402104297843
DTSTART:20230113T220000Z
DTEND:20230113T230000Z
DESCRIPTION:Enrollment: Limited: Advance sign-up required Limited to 35 par
 ticipants\n\nAttendance: Participants must attend all sessions\n\nPrereq: 
 Matrix Mathematics\n\nBig Data describes a new era in the digital age wher
 e the volume\, velocity\, and variety of data created across a wide range 
 of fields is increasing at a rate well beyond our ability to analyze the d
 ata.  Machine Learning has emerged as a powerful tool for transforming thi
 s data into usable information.  Many technologies (e.g.\, spreadsheets\, 
 databases\, graphs\, matrices\, deep neural networks\, ...) have been deve
 loped to address these challenges.  The common theme amongst these technol
 ogies is the need to store and operate on data as tabular collections inst
 ead of as individual data elements.  This class describes the common mathe
 matical foundation of these tabular collections (associative arrays) that 
 apply across a wide range of applications and technologies.  Associative a
 rrays unify and simplify Big Data and Machine Learning.  Understanding the
 se mathematical foundations allows the student to see past the differences
  that lie on the surface of Big Data and Machine Learning applications and
  technologies and leverage their core mathematical similarities to solve t
 he hardest Big Data and Machine Learning challenges.\n\nThis interactive c
 ourse will involve significant interactive student participation and a sma
 ll amount of homework.   Those students who fully participate and complete
  the homework will receive a certificate of completion.\n\nThe MIT Press b
 ook "Mathematics of Big Data" that will be used throughout the course will
  be provided.\n\nE-mail the instructor to sign up.\n\nInstructors:\n\nHayd
 en Jananthan - Research Scientist MIT Supercomputing Center - hayden.janan
 than@ll.mit.edu\n\nJeremy Kepner - Fellow & Head MIT Supercomputing Center
  - kepner@ll.mit.edu\n\nSignup Deadline: Dec 14\n\nDates:\n\nJan 10 Tue 10
 :00AM-11:55AM Virtual Course Intro and Chapter 1\n\nJan 13 Fri 05:00PM-06:
 00PM Virtual Chapters 2 & 4 Team Prep\n\nJan 17 Tue 10:00AM-11:55AM Virtua
 l Chapters 2 & 4\n\nJan 23 Mon 05:00PM-06:00PM Virtual Chapters 5 & 6 Team
  Prep\n\nJan 24 Tue 10:00AM-11:55AM Virtual Chapters 5 & 6\n\nJan 30 Mon 0
 5:00PM-06:00PM Virtual Chapters 7 & 8 Team Prep\n\nJan 31 Tue 10:00AM-11:5
 5AM Virtual Chapters 7 & 8
LOCATION:
SUMMARY:Mathematics of Big Data & Machine Learning
URL;VALUE=URI:https://calendar.mit.edu/event/mathematics_of_big_data_machin
 e_learning_2023
CATEGORIES:Conferences/Seminars/Lectures
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260906T153127Z
UID:tag:localist.com\,2008:EventInstance_41402104299892
DTSTART:20230118T030000Z
DTEND:20230118T165500Z
DESCRIPTION:Enrollment: Limited: Advance sign-up required Limited to 35 par
 ticipants\n\nAttendance: Participants must attend all sessions\n\nPrereq: 
 Matrix Mathematics\n\nBig Data describes a new era in the digital age wher
 e the volume\, velocity\, and variety of data created across a wide range 
 of fields is increasing at a rate well beyond our ability to analyze the d
 ata.  Machine Learning has emerged as a powerful tool for transforming thi
 s data into usable information.  Many technologies (e.g.\, spreadsheets\, 
 databases\, graphs\, matrices\, deep neural networks\, ...) have been deve
 loped to address these challenges.  The common theme amongst these technol
 ogies is the need to store and operate on data as tabular collections inst
 ead of as individual data elements.  This class describes the common mathe
 matical foundation of these tabular collections (associative arrays) that 
 apply across a wide range of applications and technologies.  Associative a
 rrays unify and simplify Big Data and Machine Learning.  Understanding the
 se mathematical foundations allows the student to see past the differences
  that lie on the surface of Big Data and Machine Learning applications and
  technologies and leverage their core mathematical similarities to solve t
 he hardest Big Data and Machine Learning challenges.\n\nThis interactive c
 ourse will involve significant interactive student participation and a sma
 ll amount of homework.   Those students who fully participate and complete
  the homework will receive a certificate of completion.\n\nThe MIT Press b
 ook "Mathematics of Big Data" that will be used throughout the course will
  be provided.\n\nE-mail the instructor to sign up.\n\nInstructors:\n\nHayd
 en Jananthan - Research Scientist MIT Supercomputing Center - hayden.janan
 than@ll.mit.edu\n\nJeremy Kepner - Fellow & Head MIT Supercomputing Center
  - kepner@ll.mit.edu\n\nSignup Deadline: Dec 14\n\nDates:\n\nJan 10 Tue 10
 :00AM-11:55AM Virtual Course Intro and Chapter 1\n\nJan 13 Fri 05:00PM-06:
 00PM Virtual Chapters 2 & 4 Team Prep\n\nJan 17 Tue 10:00AM-11:55AM Virtua
 l Chapters 2 & 4\n\nJan 23 Mon 05:00PM-06:00PM Virtual Chapters 5 & 6 Team
  Prep\n\nJan 24 Tue 10:00AM-11:55AM Virtual Chapters 5 & 6\n\nJan 30 Mon 0
 5:00PM-06:00PM Virtual Chapters 7 & 8 Team Prep\n\nJan 31 Tue 10:00AM-11:5
 5AM Virtual Chapters 7 & 8
LOCATION:
SUMMARY:Mathematics of Big Data & Machine Learning
URL;VALUE=URI:https://calendar.mit.edu/event/mathematics_of_big_data_machin
 e_learning_2023
CATEGORIES:Conferences/Seminars/Lectures
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260906T153127Z
UID:tag:localist.com\,2008:EventInstance_41402104300917
DTSTART:20230123T220000Z
DTEND:20230123T230000Z
DESCRIPTION:Enrollment: Limited: Advance sign-up required Limited to 35 par
 ticipants\n\nAttendance: Participants must attend all sessions\n\nPrereq: 
 Matrix Mathematics\n\nBig Data describes a new era in the digital age wher
 e the volume\, velocity\, and variety of data created across a wide range 
 of fields is increasing at a rate well beyond our ability to analyze the d
 ata.  Machine Learning has emerged as a powerful tool for transforming thi
 s data into usable information.  Many technologies (e.g.\, spreadsheets\, 
 databases\, graphs\, matrices\, deep neural networks\, ...) have been deve
 loped to address these challenges.  The common theme amongst these technol
 ogies is the need to store and operate on data as tabular collections inst
 ead of as individual data elements.  This class describes the common mathe
 matical foundation of these tabular collections (associative arrays) that 
 apply across a wide range of applications and technologies.  Associative a
 rrays unify and simplify Big Data and Machine Learning.  Understanding the
 se mathematical foundations allows the student to see past the differences
  that lie on the surface of Big Data and Machine Learning applications and
  technologies and leverage their core mathematical similarities to solve t
 he hardest Big Data and Machine Learning challenges.\n\nThis interactive c
 ourse will involve significant interactive student participation and a sma
 ll amount of homework.   Those students who fully participate and complete
  the homework will receive a certificate of completion.\n\nThe MIT Press b
 ook "Mathematics of Big Data" that will be used throughout the course will
  be provided.\n\nE-mail the instructor to sign up.\n\nInstructors:\n\nHayd
 en Jananthan - Research Scientist MIT Supercomputing Center - hayden.janan
 than@ll.mit.edu\n\nJeremy Kepner - Fellow & Head MIT Supercomputing Center
  - kepner@ll.mit.edu\n\nSignup Deadline: Dec 14\n\nDates:\n\nJan 10 Tue 10
 :00AM-11:55AM Virtual Course Intro and Chapter 1\n\nJan 13 Fri 05:00PM-06:
 00PM Virtual Chapters 2 & 4 Team Prep\n\nJan 17 Tue 10:00AM-11:55AM Virtua
 l Chapters 2 & 4\n\nJan 23 Mon 05:00PM-06:00PM Virtual Chapters 5 & 6 Team
  Prep\n\nJan 24 Tue 10:00AM-11:55AM Virtual Chapters 5 & 6\n\nJan 30 Mon 0
 5:00PM-06:00PM Virtual Chapters 7 & 8 Team Prep\n\nJan 31 Tue 10:00AM-11:5
 5AM Virtual Chapters 7 & 8
LOCATION:
SUMMARY:Mathematics of Big Data & Machine Learning
URL;VALUE=URI:https://calendar.mit.edu/event/mathematics_of_big_data_machin
 e_learning_2023
CATEGORIES:Conferences/Seminars/Lectures
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260906T153127Z
UID:tag:localist.com\,2008:EventInstance_41402104302966
DTSTART:20230124T150000Z
DTEND:20230124T165500Z
DESCRIPTION:Enrollment: Limited: Advance sign-up required Limited to 35 par
 ticipants\n\nAttendance: Participants must attend all sessions\n\nPrereq: 
 Matrix Mathematics\n\nBig Data describes a new era in the digital age wher
 e the volume\, velocity\, and variety of data created across a wide range 
 of fields is increasing at a rate well beyond our ability to analyze the d
 ata.  Machine Learning has emerged as a powerful tool for transforming thi
 s data into usable information.  Many technologies (e.g.\, spreadsheets\, 
 databases\, graphs\, matrices\, deep neural networks\, ...) have been deve
 loped to address these challenges.  The common theme amongst these technol
 ogies is the need to store and operate on data as tabular collections inst
 ead of as individual data elements.  This class describes the common mathe
 matical foundation of these tabular collections (associative arrays) that 
 apply across a wide range of applications and technologies.  Associative a
 rrays unify and simplify Big Data and Machine Learning.  Understanding the
 se mathematical foundations allows the student to see past the differences
  that lie on the surface of Big Data and Machine Learning applications and
  technologies and leverage their core mathematical similarities to solve t
 he hardest Big Data and Machine Learning challenges.\n\nThis interactive c
 ourse will involve significant interactive student participation and a sma
 ll amount of homework.   Those students who fully participate and complete
  the homework will receive a certificate of completion.\n\nThe MIT Press b
 ook "Mathematics of Big Data" that will be used throughout the course will
  be provided.\n\nE-mail the instructor to sign up.\n\nInstructors:\n\nHayd
 en Jananthan - Research Scientist MIT Supercomputing Center - hayden.janan
 than@ll.mit.edu\n\nJeremy Kepner - Fellow & Head MIT Supercomputing Center
  - kepner@ll.mit.edu\n\nSignup Deadline: Dec 14\n\nDates:\n\nJan 10 Tue 10
 :00AM-11:55AM Virtual Course Intro and Chapter 1\n\nJan 13 Fri 05:00PM-06:
 00PM Virtual Chapters 2 & 4 Team Prep\n\nJan 17 Tue 10:00AM-11:55AM Virtua
 l Chapters 2 & 4\n\nJan 23 Mon 05:00PM-06:00PM Virtual Chapters 5 & 6 Team
  Prep\n\nJan 24 Tue 10:00AM-11:55AM Virtual Chapters 5 & 6\n\nJan 30 Mon 0
 5:00PM-06:00PM Virtual Chapters 7 & 8 Team Prep\n\nJan 31 Tue 10:00AM-11:5
 5AM Virtual Chapters 7 & 8
LOCATION:
SUMMARY:Mathematics of Big Data & Machine Learning
URL;VALUE=URI:https://calendar.mit.edu/event/mathematics_of_big_data_machin
 e_learning_2023
CATEGORIES:Conferences/Seminars/Lectures
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260906T153127Z
UID:tag:localist.com\,2008:EventInstance_41402154585461
DTSTART:20230130T220000Z
DTEND:20230130T230000Z
DESCRIPTION:Enrollment: Limited: Advance sign-up required Limited to 35 par
 ticipants\n\nAttendance: Participants must attend all sessions\n\nPrereq: 
 Matrix Mathematics\n\nBig Data describes a new era in the digital age wher
 e the volume\, velocity\, and variety of data created across a wide range 
 of fields is increasing at a rate well beyond our ability to analyze the d
 ata.  Machine Learning has emerged as a powerful tool for transforming thi
 s data into usable information.  Many technologies (e.g.\, spreadsheets\, 
 databases\, graphs\, matrices\, deep neural networks\, ...) have been deve
 loped to address these challenges.  The common theme amongst these technol
 ogies is the need to store and operate on data as tabular collections inst
 ead of as individual data elements.  This class describes the common mathe
 matical foundation of these tabular collections (associative arrays) that 
 apply across a wide range of applications and technologies.  Associative a
 rrays unify and simplify Big Data and Machine Learning.  Understanding the
 se mathematical foundations allows the student to see past the differences
  that lie on the surface of Big Data and Machine Learning applications and
  technologies and leverage their core mathematical similarities to solve t
 he hardest Big Data and Machine Learning challenges.\n\nThis interactive c
 ourse will involve significant interactive student participation and a sma
 ll amount of homework.   Those students who fully participate and complete
  the homework will receive a certificate of completion.\n\nThe MIT Press b
 ook "Mathematics of Big Data" that will be used throughout the course will
  be provided.\n\nE-mail the instructor to sign up.\n\nInstructors:\n\nHayd
 en Jananthan - Research Scientist MIT Supercomputing Center - hayden.janan
 than@ll.mit.edu\n\nJeremy Kepner - Fellow & Head MIT Supercomputing Center
  - kepner@ll.mit.edu\n\nSignup Deadline: Dec 14\n\nDates:\n\nJan 10 Tue 10
 :00AM-11:55AM Virtual Course Intro and Chapter 1\n\nJan 13 Fri 05:00PM-06:
 00PM Virtual Chapters 2 & 4 Team Prep\n\nJan 17 Tue 10:00AM-11:55AM Virtua
 l Chapters 2 & 4\n\nJan 23 Mon 05:00PM-06:00PM Virtual Chapters 5 & 6 Team
  Prep\n\nJan 24 Tue 10:00AM-11:55AM Virtual Chapters 5 & 6\n\nJan 30 Mon 0
 5:00PM-06:00PM Virtual Chapters 7 & 8 Team Prep\n\nJan 31 Tue 10:00AM-11:5
 5AM Virtual Chapters 7 & 8
LOCATION:
SUMMARY:Mathematics of Big Data & Machine Learning
URL;VALUE=URI:https://calendar.mit.edu/event/mathematics_of_big_data_machin
 e_learning_2023
CATEGORIES:Conferences/Seminars/Lectures
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260906T153127Z
UID:tag:localist.com\,2008:EventInstance_41402154586486
DTSTART:20230131T150000Z
DTEND:20230131T165500Z
DESCRIPTION:Enrollment: Limited: Advance sign-up required Limited to 35 par
 ticipants\n\nAttendance: Participants must attend all sessions\n\nPrereq: 
 Matrix Mathematics\n\nBig Data describes a new era in the digital age wher
 e the volume\, velocity\, and variety of data created across a wide range 
 of fields is increasing at a rate well beyond our ability to analyze the d
 ata.  Machine Learning has emerged as a powerful tool for transforming thi
 s data into usable information.  Many technologies (e.g.\, spreadsheets\, 
 databases\, graphs\, matrices\, deep neural networks\, ...) have been deve
 loped to address these challenges.  The common theme amongst these technol
 ogies is the need to store and operate on data as tabular collections inst
 ead of as individual data elements.  This class describes the common mathe
 matical foundation of these tabular collections (associative arrays) that 
 apply across a wide range of applications and technologies.  Associative a
 rrays unify and simplify Big Data and Machine Learning.  Understanding the
 se mathematical foundations allows the student to see past the differences
  that lie on the surface of Big Data and Machine Learning applications and
  technologies and leverage their core mathematical similarities to solve t
 he hardest Big Data and Machine Learning challenges.\n\nThis interactive c
 ourse will involve significant interactive student participation and a sma
 ll amount of homework.   Those students who fully participate and complete
  the homework will receive a certificate of completion.\n\nThe MIT Press b
 ook "Mathematics of Big Data" that will be used throughout the course will
  be provided.\n\nE-mail the instructor to sign up.\n\nInstructors:\n\nHayd
 en Jananthan - Research Scientist MIT Supercomputing Center - hayden.janan
 than@ll.mit.edu\n\nJeremy Kepner - Fellow & Head MIT Supercomputing Center
  - kepner@ll.mit.edu\n\nSignup Deadline: Dec 14\n\nDates:\n\nJan 10 Tue 10
 :00AM-11:55AM Virtual Course Intro and Chapter 1\n\nJan 13 Fri 05:00PM-06:
 00PM Virtual Chapters 2 & 4 Team Prep\n\nJan 17 Tue 10:00AM-11:55AM Virtua
 l Chapters 2 & 4\n\nJan 23 Mon 05:00PM-06:00PM Virtual Chapters 5 & 6 Team
  Prep\n\nJan 24 Tue 10:00AM-11:55AM Virtual Chapters 5 & 6\n\nJan 30 Mon 0
 5:00PM-06:00PM Virtual Chapters 7 & 8 Team Prep\n\nJan 31 Tue 10:00AM-11:5
 5AM Virtual Chapters 7 & 8
LOCATION:
SUMMARY:Mathematics of Big Data & Machine Learning
URL;VALUE=URI:https://calendar.mit.edu/event/mathematics_of_big_data_machin
 e_learning_2023
CATEGORIES:Conferences/Seminars/Lectures
END:VEVENT
END:VCALENDAR