Toyota Technological Institute at Chicago (TTIC) Courses and Prerequisites for MPCS Students

Satisfies: Core Theory, Elective

Prerequisites:

• A- or higher in MPCS 50103 Discrete Math (Immersion Math) OR successfully passing the Mathematics Placement exam.
• Core Programming (completed or concurrently taking).

Satisfies: Data Analytics specialization, Elective

Prerequisites: The instructor for this class administers an exam for non-TTIC students on the first day of class. This exam covers the following:

– Linear algebra: vectors, matrices, linear independence, orthogonality, determinants, matrix inverses
– Calculus: derivatives, partial derivatives
– Probability: random variables, conditional probabilities, independence, conditional independence, discrete and continuous random variables, multivariate Gaussians, expectation, variance

This exam is mandatory, and the instructor will *not* waive it based on past coursework. The MPCS won’t require any specific MPCS coursework before taking this class, but we expect that students who have passed MPCS 53110 Foundations of Computational Data Analysis or passed the MPCS Data Placement Exam should be able to pass the TTIC 31020 first-day exam.

The instructor encourages students interested in this class to e-mail him so they can be kept in the loop of any updates regarding the class or the first-day exam.

Satisfies: Data Analytics specialization, Elective

Prerequisites: TTIC 31020 Introduction to Machine Learning or MPCS 53111 Machine Learning

The instructor has also indicated that students who have not taken either of the above courses, but have a comparable ML background, should reach out to the instructor to discuss their enrollment.

Satisfies: Elective

Prerequisite: Core Programming. Please note that this class specifically assumes familiarity with Python, Java, or C/C++

Satisfies: Elective

Prerequisites: The prerequisites for this class can be fulfilled one of two ways:

1. B or higher in STAT 30900 Matrix Computation
2. A- or higher in MPCS 55001 Algorithms, and having taken linear algebra and vector calculus in the past (students who have taken CMSC 25300 Mathematical Foundations of Machine Learning should have the right background)

All students must also complete a take-home evaluation during the first week of the quarter. The instructor may decline students on the basis of this evaluation, regardless of their background.

Satisfies: Core Theory, Elective

Prerequisite: MPCS 55001 Algorithms

Satisfies: Elective

Prerequisites:

• MPCS 50103 Discrete Math (Immersion Math) OR successfully passing the Mathematics Placement exam.
• Core Programming (completed or concurrently taking).
• [Recommended] MPCS 53111 Machine Learning

Students also need to have a good background in probability, which includes both continuous and discrete variables and some linear algebra that is needed to deal with multivariate distributions. Students who are unsure of whether they have the expected background should reach out to the instructor.

Satisfies: Elective

Prerequisites: This class has no course prerequisites, but the instructor requires an in-person meeting before granting consent to take the class. You must meet with the instructor before you request consent from the MPCS to take this class.

Satisfies: Data Analytics specialization, Elective

Prerequisites:

• TTIC 31020 Introduction to Machine Learning or MPCS 53111 Machine Learning
• This class relies heavily on linear algebra and multivariate (vector) calculus. While there is no specific class prerequisite, students without a background in these topics are not likely to succeed in this class.

The instructor has also indicated that students with a very strong mathematics background may be able to succeed in the class even if they haven’t taken a Machine Learning class. Such students must get consent from the instructor to waive the prerequisite and take the class.

Satisfies: Data Analytics specialization, Elective

Prerequisites:

• TTIC 31020 Introduction to Machine Learning or MPCS 53111 Machine Learning
• This class relies heavily on linear algebra and multivariate (vector) calculus. While there is no specific class prerequisite, students without a background in these topics are not likely to succeed in this class.

The instructor has also indicated that students with a very strong mathematics background may be able to succeed in the class even if they haven’t taken a Machine Learning class. Such students must get consent from the instructor to waive the prerequisite and take the class.