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MIT Subject Listing & Schedule
My Course Selections

6.9280[J] Leading Creative Teams
______

Graduate (Fall, Spring)
(Same subject as 15.674[J], 16.990[J])
Prereq: Permission of instructor
Units: 3-0-6
Remove from schedule Lecture: MW2.30-4 (4-163)
______
Prepares students to lead teams charged with developing creative solutions in engineering and technical environments. Grounded in research but practical in focus, equips students with leadership competencies such as building self-awareness, motivating and developing others, creative problem solving, influencing without authority, managing conflict, and communicating effectively. Teamwork skills include how to convene, launch, and develop various types of teams, including project teams. Learning methods emphasize personalized and experiential skill development. Enrollment limited.
D. Nino
No textbook information available

18.725 Algebraic Geometry I
______

Graduate (Fall)
Prereq: None. Coreq: 18.705
Units: 3-0-9
Remove from schedule Lecture: MWF9 (2-136)
______
Introduces the basic notions and techniques of modern algebraic geometry. Covers fundamental notions and results about algebraic varieties over an algebraically closed field; relations between complex algebraic varieties and complex analytic varieties; and examples with emphasis on algebraic curves and surfaces. Introduction to the language of schemes and properties of morphisms. Knowledge of elementary algebraic topology, elementary differential geometry recommended, but not required.
B. Poonen
No required or recommended textbooks

18.726 Algebraic Geometry II
______

Graduate (Spring)
Prereq: 18.725
Units: 3-0-9
______
Continuation of the introduction to algebraic geometry given in 18.725. More advanced properties of the varieties and morphisms of schemes, as well as sheaf cohomology.
Staff

16.31 Feedback Control Systems
______

Graduate (Fall)
(Subject meets with 16.30)
Prereq: 16.06 or permission of instructor
Units: 3-1-8
Remove from schedule Lecture: MW2.30-4 (32-144) Lab: TBA +final
______
Graduate-level version of 16.30; see description under 16.30. Includes additional homework questions, laboratory experiments, and a term project beyond 16.30 with a particular focus on the material associated with state-space realizations of MIMO transfer function (matrices); MIMO zeros, controllability, and observability; stochastic processes and estimation; limitations on performance; design and analysis of dynamic output feedback controllers; and robustness of multivariable control systems.
C. Fan
No textbook information available

7.89[J] Topics in Computational and Systems Biology
______

Graduate (Fall)
(Same subject as CSB.100[J])
Prereq: Permission of instructor
Units: 2-0-10
Remove from schedule Lecture: F11-1 (66-148)
______
Seminar based on research literature. Papers covered are selected to illustrate important problems and varied approaches in the field of computational and systems biology, and to provide students a framework from which to evaluate new developments. Preference to first-year CSB PhD students.
C. Burge
No textbook information available

9.918 BCS Grant Writing Workshop
______

Graduate (Fall)
Prereq: None
Units: 1-0-0
Remove from schedule TBA.
______
Fellowship writing workshop to develop applications for predoctoral fellowships, including the NSF and NDSEG programs.
Kanwisher, Nancy 
No textbook information available

9.401 Tools for Robust Science
______

Graduate (Fall)
Prereq: None
Units: 3-0-9
Remove from schedule Lecture: T9-12 (46-3037)
______
New tools are being developed to improve credibility, facilitate collaboration, accelerate scientific discovery, and expedite translation of results. Students (i) identify obstacles to conducting robust cognitive and neuroscientific research, (ii) practice using current cutting-edge tools designed to overcome these obstacles by improving scientific practices and incentives, and (iii) critically evaluate these tools' potential and limitations. Example tools investigated include shared pre-registration, experimental design, data management plans, meta-data standards, repositories, FAIR code, open-source data processing pipelines, alternatives to scientific paper formats, alternative publishing agreements, citation audits, reformulated incentives for hiring and promotion, and more. 
R. Saxe
No textbook information available

7.THG Graduate Biology Thesis
______

Graduate (Fall, IAP, Spring, Summer) Can be repeated for credit
Prereq: Permission of instructor
Units arranged
Remove from schedule TBA.
______
Program of research leading to the writing of a Ph.D. thesis; to be arranged by the student and an appropriate MIT faculty member.
Fall: Staff
IAP: Staff
Spring: Staff
Summer: Staff
No required or recommended textbooks

IDS.955 Practical Experience in Data, Systems, and Society
______

Graduate (Fall, IAP, Spring, Summer) Can be repeated for credit
Prereq: None
Units arranged [P/D/F]
Remove from schedule TBA.
______
For IDSS doctoral students participating in off-campus practical experiences in data, systems, and society. Before registering for this subject students must have a training offer from a company or organization, must identify a research advisor, and must receive prior approval from the IDSS Academic Office. Upon completion of the experience students must submit a letter from the company or organization describing the goals accomplished and a substantive final report to the MIT advisor.
E. Milnes
No textbook information available

15.095 Machine Learning Under a Modern Optimization Lens
______

Graduate (Fall)
Prereq: 6.7210, 15.093, or permission of instructor
Units: 3-1-8
Sloan bid You must participate in Sloan's Course Bidding to take this subject.
Remove from schedule Lecture: MW4-5.30 (E51-345) Recitation: F11 (E51-345)
______
Develops algorithms for central problems in machine learning from a modern optimization perspective. Topics include sparse, convex, robust and median regression; an algorithmic framework for regression; optimal classification and regression trees, and their relationship with neural networks; how to transform predictive algorithms to prescriptive algorithms; optimal prescriptive trees; and robust classification.  Also covers design of experiments, missing data imputations, mixture of Gaussian models, exact bootstrap, and sparse matrix estimation, including principal component analysis, factor analysis, inverse co-variance matrix estimation, and matrix completion.
K. Villalobos Carballo
Textbooks (Fall 2024)

6.9850 6-A Internship
______

Undergrad (Fall, Spring, Summer)
Prereq: None
Units: 0-12-0 [P/D/F]
Remove from schedule TBA.
______
Provides academic credit for the first assignment of 6-A undergraduate students at companies affiliated with the department's 6-A internship program. Limited to students participating in the 6-A internship program.
P. Capistrano
No textbook information available

15.280 Communication for Leaders
______

Graduate (Fall)
Prereq: Permission of instructor
Units: 3-1-5
Credit cannot also be received for 15.710
Sloan bid You must participate in Sloan's Course Bidding to take this subject.
Remove from schedule Lecture: T1-2.30 (E62-223, E51-335) or T2.30-4 (E51-325, E51-145) or T1-2.30 (E51-325, E51-145) or T4-5.30 (E51-149, E62-262, E51-335, E62-223) or T2.30-4 (E62-223, E51-335)
______
Students develop and polish communication strategies and methods through discussion, examples, and practice. Emphasizes writing and speaking skills necessary for effective leaders. Includes several oral and written assignments which are integrated with other subjects, and with career development activities, when possible. Schedule and curriculum coordinated with Organizational Processes. Mandatory one hour recitation in small groups. Restricted to first-year Sloan graduate students.
N. Hartman
No textbook information available

16.990[J] Leading Creative Teams
______

Graduate (Fall, Spring)
(Same subject as 6.9280[J], 15.674[J])
Prereq: Permission of instructor
Units: 3-0-6
Remove from schedule Lecture: MW2.30-4 (4-163)
______
Prepares students to lead teams charged with developing creative solutions in engineering and technical environments. Grounded in research but practical in focus, equips students with leadership competencies such as building self-awareness, motivating and developing others, creative problem solving, influencing without authority, managing conflict, and communicating effectively. Teamwork skills include how to convene, launch, and develop various types of teams, including project teams. Learning methods emphasize personalized and experiential skill development. Enrollment limited.
D. Nino
No textbook information available

Total units: 100+

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TIMEMon TueWed ThuFri KEY

 6.9280

 18.725

 16.31

 7.89

 9.918

 9.401

 7.THG

 IDS.955

 15.095

 6.9850

 15.280

 16.990

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