IAP 2023 Classes

• For credit subjects
• Non-credit activities and subjects

For-credit 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 Bill Minicozzi and staff
• Jan. 9 - Feb. 3
• MTWRF1
• TR10-11.30 or TR2-3.30 +final

This class will meet in person on campus. Lectures will be held in E25-111.

12 units (only 6 will count toward IAP credit limit)

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 Keaton Burns
• Jan. 9-27
• MWF 10-12
• This class will be conducted in hybrid mode, with students encouraged to attend in person in room 2-142, but a zoom option will be made available.

3 units (P/D/F graded)

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

• MWF1-2.30
• R10.30-12 or R1-2.30
• This class will meet in person on campus. Lectures will be held in 2-190, and many should also be recorded. Recitations will meet in 2-131.

6 units (P/D/F graded)

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.

Lecture Schedule

18.S096 Special Subject in Mathematics: Matrix Calculus for Machine Learning and Beyond

• Website: https://github.com/mitmath/matrixcalc/blob/main/README.md
• Profs Alan Edelman and Steven Johnson
• Jan 17 - Feb 3
• MWF 11am-1pm
• This class will meet in 2-190.

3 units

We all know that calculus courses such as 18.01 and 18.02 are univariate and vector calculus, respectively. Modern applications such as machine learning require the next big step, matrix calculus.

This class covers a coherent approach to matrix calculus showing techniques that allow you to think of a matrix holistically (not just as an array of scalars), compute derivatives of important matrix factorizations, and really understand forward and reverse modes of differentiation. We will discuss adjoint methods, custom Jacobian matrix vector products, and how modern automatic differentiation is more computer science than mathematics in that it is neither symbolic nor finite differences.

Prereq: Linear Algebra such as 18.06 and multivariate calculus such as 18.02

18.S097 Special Subject in Mathematics: Introduction to Julia for Data Science

• Profs Alan Edelman, Bogumił Kamiński, and Przemysław Szufel
• Jan 17 - 20
• TWRF 11am-12:30; 1pm-3pm
• This class will meet in 2-131.

3 units

Data analysis has become one of the core processes in virtually any professional activity. The collection of data becomes easier and less expensive, so we have ample access to it.

The Julia language which was designed to address the typical challenges that data scientists face when using other tools. Julia, like Python, supports an efficient and convenient development process. At the same time, programs developed in Julia have performance comparable to C.

During this short course you will learn how to build data science models using Julia. Moreover, we will teach you how to deploy such model in production environments and scale the computations beyond a single computer.

This course does not require from the participants prior detailed knowledge of advanced machine learning algorithms not the Julia programming language. What we assume is basic knowledge data science tools (like Python or R) and techniques (like linear regression, basic statistics, plotting).

All course materials are available on a dedicated GitHub repository https://github.com/pszufe/MIT_18.S097_Introduction-to-Julia-for-Data-Science.

Schedule

Location: Room 2-131 on MIT Campus. See http://whereis.mit.edu/?mapterms=2-131 for location.

Instructors: Bogumił Kamiński, Łukasz Kraiński, Przemysław Szufel, Bartosz Witkowski, Sebastian Zając, Mateusz Zawisza

Grading

You can register for this course for credit. The contact point regarding the registration process is Professor Alan Edelman, Julia Lab Research Group Leader.

The evaluation of the course will be based on assessment of a homework that will be distributed during the last day of the course and should be sent back to Przemysław Szufel (