ESE 2040, Fall 2023 – Decision Models (and Algorithms)
Instructor: Nikolai Matni, Assistant Professor, ESE Department
Teaching assistant: TBD
Graders: TBD
Lectures: Tu/Th 1:453:15pm ET, ANNS 111
Office hours: NM: Tu 3:304:30pm ET, Levine 374, TBD, TBD, TBD: TBD (check Canvas)
Syllabus: ESE2040 Fall 2023
Canvas: We will be using Canvas to manage class logistics. Please log in and register here. On Canvas, there will be a link to Ed Discussion, please register there as well.
Course description
This first course in decision making will introduce you to quantitative models for decision and design in the sciences, engineering, machine learning, data science, logistics, and economics. Through applicationbased case studies, you will be shown how to (i) formalize a decision problem as a mathematical optimization problem, and (ii) solve the resulting optimization problem using Python scientific computing modules. You will also be given a brief introduction to the optimization algorithms and programming tools underpinning contemporary deep learning and shown how to apply them to decision and design problems.
About the Course
Prerequisites
The only official prerequisites for this class is Math 1400. Basic familiarity with Linear Algebra (vectors, matrices) and Python and programming is helpful, but not necessary.
Tentative homework schedule
09/07: Hw1 out
09/14: Hw2 out, Hw1 due
09/21: Hw3 out, Hw2 due
09/28: Hw4 out, Hw3 due
10/05: Hw4 due, midterm study break
10/10: Inclass midterm 1, takehome midterm out
10/19: Hw5 out, takehome midterm due
10/26: Hw6 out, Hw5 due
11/02: Hw7 out, Hw6 due
11/09: Hw8 out, Hw7 due
11/16: Hw8 due, Hw9 out
11/23: Thanksgiving break
11/30: Hw9 due, final study break
12/07: Inclass midterm 2, takehome midterm out
Grading
Homework (50%): there will be 9 homework assignments. They will be assigned weekly, handed out on Thursday at 1pm and due the following Thursday at 1pm. There will be suitable breaks in assignments to accommodate exams and the Thanksgiving holiday weekend. Assignments will include both conceptual (written) and implementation (programming) exercises. You will be given 5 free late days which you may use as you please throughout the semester, after which no late assignments will be accepted. Each homework problem will be graded on a scale of 02: no points are awarded for a skipped problem, 1 point for a solid attempt, and 2 points for a mostly correct solution.
Midterm exam 1 (25%): the midterm will consist of an inclass written component (15%) and a takehome computational component (10%). The inclass component will be closedbook and closednotes. However, you will be allowed a single sheet of standardsized paper with you with anything you want written on it (doublesided). No electronic devices are allowed. The takehome component of the exam will be open book.
Midterm exam 2 (25%): the midterm will consist of an inclass written component on the last day of classes (15%) and a takehome computational component (10%) due one week later. The inclass component will be closedbook and closednotes. However, you will be allowed a single sheet of standardsized paper with you with anything you want written on it (doublesided). No electronic devices are allowed. The takehome component of the exam will be open book.
Code of Academic Integrity: All students are expected to adhere to the University’s Code of Academic Integrity.
