= Add activity session to your calendar (exports in iCalendar format)
Integration Bee
Samuel Elder
Enrollment: Unlimited: Advance sign-up required
Sign-up by 01/23
Attendance: Contestants must qualify. See Tues, Jan. 23
Prereq: need to pass the qualifying test on 1/23 to enter the Bee
See individual session descriptions below.
Sponsor(s): Mathematics
Contact: Samuel Elder, 2-340A, same@math.mit.edu
|
Jan/23 | Tue | 04:00PM-06:00PM | Room 4-149 |
Stop by at any point during the session, for a quick test of your single variable integration skills. Top scorers qualify for the Integration Bee. No knowledge beyond 18.01 necessary.
http://www.mit.edu/~same/integrationbee.html
Samuel Elder
|
Jan/25 | Thu | 07:00PM-10:00PM | Room 26-100 |
No enrollment limit. No advance sign up (but contestants must qualify, during the testing on January 23rd). Come watch your fellow students match wits and single variable integration skills for prizes and the title of "Grand Integrator".
http://www.mit.edu/~same/integrationbee.html
Samuel Elder
Mathematics Department Music Recital
Gweneth McKinley
Enrollment: Limited: Advance sign-up required
Sign-up by 01/17
Attendance: Participants must attend all sessions
We would like to invite you to *perform* in this year's Mathematics IAP recital. Please join us for a cozy afternoon of wonderful music at the end of winter break. All genres, compositions, and ensemble sizes are welcome!
If you decide that this is in your future, please email me by next Wednesday (January 17th), and let me know:
- the instruments
- the title, composer, and approximate duration of the piece(s)
- any specific needs (microphones, outlets, page turners, etc.)
There will be a rehearsal/sound check in Room 2-470 on Monday, January 29th from 3-7pm.
For all inquiries please feel free to contact me!
Sponsor(s): Mathematics
Contact: Gweneth McKinley, 2-155, gweneth@mit.edu
|
Jan/29 | Mon | 03:00PM-07:00PM | 2-470, REQUIRED Rehearsal |
|
Jan/31 | Wed | 03:30PM-05:30PM | 2-470, Recital |
Contact me for all inquiries!
Gweneth McKinley
Mathematics Lecture Series
Alan Edelman
Enrollment: Unlimited: No advance sign-up
Attendance:
Ten lectures by Mathematics faculty members on interesting topics from both classical and modern Mathematics. All lectures should be accessible to students with a Calculus background and an interest in Mathematics. These lectures are open to the public and you may attend as many or as few as you wish. Students wishing to receive course credit for attending the full set of 10 lectures should consider registering for the IAP for-credit subject 18.095, in which P-sets are assigned and students prepare these for discussion in a weekly problem session.
For more information on idividual lectures please see:
http://math.mit.edu/academics/iap.php
Sponsor(s): Mathematics
Contact: Alan Edelman, edelman@mit.edu
|
Jan/08 | Mon | 01:00PM-02:30PM | 2-190, Speaker: Heather Macbeth |
|
Jan/10 | Wed | 01:00PM-02:30PM | 2-190, Speaker: Gil Strang |
|
Jan/12 | Fri | 01:00PM-02:30PM | 2-190, Speaker: Steven Johnson |
|
Jan/17 | Wed | 01:00PM-02:30PM | 2-190, Speaker: Jeremy Kepner |
|
Jan/19 | Fri | 01:00PM-02:30PM | 2-190, Speaker: Dan Stroock |
|
Jan/22 | Mon | 01:00PM-02:30PM | 2-190, Speaker: Juan Pablo Vielma |
|
Jan/24 | Wed | 01:00PM-02:30PM | 2-190, Speaker: Thomas Beck |
|
Jan/26 | Fri | 01:00PM-02:30PM | 2-190, Speaker: Scott Sheffield |
|
Jan/29 | Mon | 01:00PM-02:30PM | 2-190, Speaker: Philippe Rogollet |
|
Jan/31 | Wed | 01:00PM-02:30PM | 2-190, Speaker: Joern Dunkel |
For more information on individual lectures please see:
http://math.mit.edu/academics/iap.php
Mathematics of Big Data & Machine Learning
Jeremy Kepner, Fellow & Head MIT Supercomputing Center
Enrollment: Limited: Advance sign-up required
Sign-up by 12/22
Limited to 20 participants
Attendance: Participants must attend all sessions
Prereq: Linear Algebra
Big Data describes a new era in the digital age where the volume, velocity, and variety of data created across a wide range of fields (e.g., internet search, healthcare, finance, social media, defense, ...) is increasing at a rate well beyond our ability to analyze the data. Machine Learning has emerged as a powerful tool for transforming this data into usable information. Many technologies (e.g., spreadsheets, databases, graphs, linear algebra, deep neural networks, ...) have been developed to address these challenges. The common theme amongst these technologies is the need to store and operate on data as whole collections instead of as individual data elements. This class describes the common mathematical foundation of these data collections (associative arrays) that apply across a wide range of applications and technologies. Associative arrays unify and simplify Big Data and Machine Learning. Understanding these 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 the hardest Big Data and Machine Learning challenges.
Sponsor(s): Mathematics
Contact: Jeremy Kepner, 2nd Floor, 300 Tech Sq, 781 981-3108, KEPNER@LL.MIT.EDU
|
Jan/09 | Tue | 11:00AM-01:00PM | 300 Tech Sq Floor 2 |
Chapters 1 and 2 of "Mathematics of Big Data" text.
Jeremy Kepner - Fellow & Head MIT Supercomputing Center
|
Jan/16 | Tue | 11:00AM-01:00PM | 300 Tech Sq Floor 2 |
Chapter 3 and Chapter 4 of "Mathematics of Big Data" text.
Introduction to D4M (http://d4m.mit.edu).
Jeremy Kepner - Fellow & Head MIT Supercomputing Center
|
Jan/23 | Tue | 11:00AM-01:00PM | 300 Tech Sq Floor 2 |
Chapter 5 and 6 of "Mathematics of Big Data" text.
Jeremy Kepner - Fellow & Head MIT Supercomputing Center
|
Jan/30 | Tue | 11:00AM-01:00PM | 300 Tech Sq Floor 2 |
Chapters 7 and 8 of "Mathematics of Big Data".
Jeremy Kepner - Fellow & Head MIT Supercomputing Center
Contact Information
COPYRIGHT 2018