IAP 2019 Classes
Noncredit activities and classes:
Check out the IAP pages at http://web.mit.edu/iap/listings/
Forcredit subjects:
Check out the course catalog at http://student.mit.edu/catalog/m18a.html. You can use the Subject Search functionality to limit the search to IAP listings or find Math's IAP offerings here: http://student.mit.edu/catalog/search.cgi?search=18&when=J. Our main offerings in Mathematics are:
18.02A Calculus
Prof Davesh Maulik and staff
Dates: Jan. 7  Feb. 1
Lectures: MTWRF12
Recitation: TR1011.30 (2132, 2136, 2142) or TR23.30 (2132, 2136, 2146)
+final
Room: 54100
This is the second half of 18.02A and can be taken only by students who took the first half in the fall term; it covers the remaining material in 18.02.
18.031 System Functions and the Laplace Transform
Dr Philip Pearce
Dates: Jan. 14  Feb. 1
Lectures: MWF 10am12noon
(with an extra meeting Tues Jan 22, in place of the MLK Day holiday on the 21st)
Room: 2131
Studies basic continuous control theory as well as representation of functions in the complex frequency domain. Covers generalized functions, unit impulse response, and convolution; and Laplace transform, system (or transfer) function, and the pole diagram. Includes examples from mechanical and electrical engineering.
18.095 Mathematics Lecture Series
Lecture: MWF12.30
Recitation: R10.3012 (2131) or R12.30 (2131)
Room: 2190
Ten lectures by mathematics faculty members on interesting topics from both classical and modern mathematics. All lectures accessible to students with calculus background and an interest in mathematics. At each lecture, reading and exercises are assigned. Students prepare these for discussion in a weekly problem session.
Here is a tentative list of speakers and dates:

Monday, January 7: Prof Vadim Gorin
Persistent random walk and the telegraph equation
Abstract: We will discuss how the evolution of a random walker on the square grid leads to a second order partial differential equation known as the telegraph equation.

Wednesday, January 9: Prof Peter Shor
Abstract:

Friday, January 11: Prof Kasso A Okoudjou
Calculus on fractals
Abstract:

Monday, January 14: Prof Gilbert Strang
The Functions of Deep Learning
Abstract: We show how the layered neural net architecture of deep learning produces continuous piecewise linear functions as approximations to the unknown map from input to output. A combinatorial formula counts the number of linear pieces in a typical learning function.

Wednesday, January 16: Prof Steven Johnson
Abstract:

Friday, January 18: Prof John Bush
Surface tension
Abstract: Surface tension is a property of fluid interfaces that leads to myriad subtle and striking effects in nature and technology. We describe a number of surfacetensiondominated systems and how to rationalize their behavior via mathematical modeling. Particular attention is given to the influence of surface tension on biological systems.

Wednesday, January 23: Dr. Jeremy Kepner
Mathematics of Big Data & Machine Learning
Abstract: 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 talk 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. Supplementary lectures, text, and software are available at: https://mitpress.mit.edu/books/mathematicsbigdata

Friday, January 25: Prof Justin Solomon
Transport, Geometry, and Computation
Abstract: Optimal transport is a mathematical tool that links probability to geometry. In this talk, we will show how transport can be brought from theory to practice, with applications in machine learning and computer graphics.

Monday, January 28: Prof Scott Sheffield
Abstract:

Wednesday, January 30: Dr. Chris Rackauckas
Abstract:
18.S097 Special Subject in Mathematics: Applied Category Theory
Drs David Spivak and Brendan Fong
Dates: Jan 14  Feb 1
Lecture: MTWRF 23
Room: 2142
Category theory is a relatively new branch of mathematics that has transformed much of pure math research. The technical advance is that category theory provides a framework in which to organize formal systems and by which to translate between them, allowing one to transfer knowledge from one field to another. But this same organizational framework also has many compelling examples outside of pure math.
In this course we provide an introductory tour of category theory, with a viewpoint toward modelling realworld phenomena. The course will begin with the notion of poset, and introduce central categorical ideas such as functor, natural transformation, (co)limit, adjunction, the adjoint functor theorem, and the Yoneda lemma in that context. We'll then move to enriched categories, profunctors, monoidal categories, operads, and toposes. Applications to resource theory, databases, codesign, signal flow graphs, and dynamical systems will help ground these notions, providing motivation and a touchstone for intuition. The aim of the course is to provide an overview of the breadth of research in applied category, so as to invite further study.