C01/C51 Modeling with Machine Learning

Level: Undergraduate (C01) and Graduate (C51)
Units: 6+6
Term(s): Spring only

Teaches students from a range of majors to translate a problem into a machine learning (ML) formulation and find appropriate tools for solving it. Students enroll in two 6-unit modules, run in tandem over the course of a full term—the common core, which covers ML fundamentals, and one of four discipline-specific modules that build on the core material.

Students take the core module:

and one of the following disciplinary modules:

Core Module

Modeling with Machine Learning: from Algorithms to Applications

Offered under: 6.C01, 6.C51
Level: Undergraduate and Graduate
Units: 6
Prerequisite: Calculus II and 6.100A
Instructors: Regina Barzilay (EECS)

Focuses on modeling with machine learning methods with an eye towards applications in engineering and sciences. Students will be introduced to modern machine learning methods, from supervised to unsupervised models, with an emphasis on newer neural approaches. Develops understanding of how and why the methods work from the point of view of modeling, and when they are applicable. Using concrete examples, covers formulation of machine learning tasks, adapting and extending methods to given problems, and how the methods can and should be evaluated. Students taking graduate version complete additional assignments. Enrollment limited.
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Disciplinary Modules

Machine Learning for Sustainable Systems

Offered under: 1.C01, 1.C51
Level: Undergraduate and Graduate
Units: 6
Prerequisite: (1.000 or 1.010) or permission of instructor
Instructors: Saurabh Amin (Civil and Environmental Engineering)

Emphasizes the design and operation of sustainable systems. Illustrates how to leverage heterogeneous data from urban services, cities, and the environment, and apply machine learning methods to evaluate and/or improve sustainability solutions. Provides case studies from various domains, such as transportation and urban mobility, energy and water resources, environmental monitoring, infrastructure sensing and control, climate adaptation, and disaster resilience. Projects focus on using machine learning to identify new insights or decisions that can help engineer sustainability in societal-scale systems. Students taking graduate version complete additional assignments.
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Physical Systems Modeling and Design Using Machine Learning

Offered under: 2.C01, 2.C51
Level: Undergraduate and Graduate
Units: 6
Prerequisite: 2.086
Instructors: George Barbastathis (Mechanical Engineering)

Encourages open-ended exploration of the increasingly topical intersection between artificial intelligence and the physical sciences. Uses energy and information, and their respective optimality conditions, to define supervised and unsupervised learning algorithms as well as ordinary and partial differential equations. Subsequently, physical systems with complex constitutive relationships are drawn from elasticity, biophysics, fluid mechanics, hydrodynamics, acoustics, and electromagnetics to illustrate how machine learning-inspired optimization can approximate solutions to forward and inverse problems in these domains. Students taking graduate version complete additional assignments.
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Machine Learning for Molecular Engineering

Offered under: 3.C01, 3.C51, 10.C01, 10.C51, 20.C01, 20.C51
Level: Undergraduate and Graduate
Units: 6
Prerequisite: Calculus II and 6.100A
Instructors: Rafael Gomez-Bombarelli (Materials Science and Engineering), Connor Coley (Chemical Engineering), Ernest Fraenkel (Biological Engineering)

Provides an introduction to the use of machine learning to solve problems arising in the science and engineering of biology, chemistry, and materials. Equips students to design and implement machine learning approaches to challenges such as analysis of omics (genomics, transcriptomics, proteomics, etc.), microscopy, spectroscopy, or crystallography data and design of new molecules and materials such as drugs, catalysts, polymer, alloys, ceramics, and proteins. Students taking graduate version complete additional assignments.
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Modeling with Machine Learning for Computer Science

Modeling with Machine Learning for Computer Science was successfully piloted in Spring 2023, under the numbers 6.S052 and 6.S952.  A proposal to add the class to the catalog, with permanent numbers 6.C011 and 6.C511, is pending approval for Spring 2024.

Level: Undergraduate and Graduate
Units: 6
Prerequisite: 18.C06; 6.1200 or 6.3700; 6.100A
Instructors: Tommi Jaakkola (EECS)

Focuses on in-depth modeling of engineering tasks as machine learning problems. Emphasizes framing, method design, and interpretation of results. In comparison to broader co-requisite 6.C01/6.C51, this project-oriented subject consists of deep dives into select technical areas or engineering tasks involving both supervised and exploratory uses of machine learning. Deep dives into technical areas, such as robustness, interpretability, privacy or causal discovery; engineering tasks such as recommender systems, performance optimization, or automated design.

Machine Learning in Molecular and Cellular Biology

The Department of Biology is developing a new pilot module for Spring 2024. The class will be taught by Joey Davis and meet with 3.C01/10.C01/20.C01, with an emphasis on protein structure and function and areas in biological imaging in which ML is enabling exciting advances.

Modeling with Machine Learning: Nuclear Science and Engineering Applications

Offered under: 22.C01, 22.C51
Level: Undergraduate and Graduate
Units: 6
Prerequisite: Calculus II and 6.100A
Instructors: Ericmoore Jossou (Nuclear Science and Engineering)

Focuses on applying various machine learning techniques to a broad range of topics which are of core value in modern nuclear science and engineering. Relevant topics include machine learning on fusion and plasma diagnosis, reactor physics and nuclear fission, nuclear materials properties, quantum engineering and nuclear materials, and nuclear security. Special components center on the additional machine learning architectures that are most relevant to a certain field, the implementation, and picking up the right problems to solve using a machine learning approach. Final project dedicated to the field-specific applications. Students taking graduate version complete additional assignments.
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Machine Learning Applications for Supply Chain

Offered under: SCM.C01, SCM.C51
Level: Graduate
Units: 6
Prerequisite: SCM.254 or permission of instructor
Instructors: Elenna Dugundji (Center for Transportation and Logistics)

Building upon 6.C51, students will design and develop applications targeted to solve key problems in Supply Chain (SC) using state-of-art machine learning (ML) methods and algorithms. Special emphasis will be given to the selection of the appropriate ML methods for the SC problems addressed, as well as requirements for code productionalization and code efficiency, both key in real industry applications.
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