EconS -315-shanna.hiscock-2024-09-16-09-41-29
Below is a syllabus template that includes WSU's required syllabus elements. Please complete all items highlighted in yellow.
Title of Course [Machine Learning for Economists]
Prefix and Number [ECONS 315]
Semester and Year [tbd]
Number of Credit Hours [3]
Prerequisites [Stat 212 or EconS 310; EconS 215; EconS 311 ]
Course Details
Day and Time: [tbd]
Meeting Location: [tbd]
Instructor Contact Information
Instructor Name: [tbd]
Instructor Contact Information: [office location, phone, email] [tbd]
Instructor Office Hours: [click here for best practices] [tbd]
TA Name: [tbd]
TA Contact Information: [office location, phone, email]: [tbd]
TA Office Hours: [click here for best practices] [tbd]
Course Description
The theory and practice of ML methods are covered with applications to the study of economic and other phenomena. The course utilizes common ML algorithms including supervised learning for both regression and classification problems, unsupervised learning for dimensionality reduction, and introduces neural networks. The course makes extensive use of the Python programming language.
Course Materials
Books: [Chirag Shah, A Hands-On Introduction to Machine Learning. Cambridge University Press: Cambridge, England. 2023. ISBN: 978-1-009-12330-3]
Other Materials: [Access to datasets (in Student Downloads Tab): https://www.cambridge.org/highereducation/books/a-hands-on-introduction-to-machine-learning/3E57313A963BF7AF5C6330EB88ADAB2E/resources/instructor-resources/32BF60E4E75F7154A3D2B62BB62088DD] Cost: $113
Fees: [None]
|
Course Learning Outcomes (students will be able to:) |
Activities Supporting the Learning Outcomes | Assessment of the Learning Outcomes |
|---|---|---|
| [Computational Thinking: Student will learn to apply the concepts of abstraction and decomposition to illuminate and quantify the impacts of important drivers on economic outcomes based on: problem formulation (abstraction and conceptualization), solution expression (modeling and programing) and, solution execution and evaluation (analysis, validity, and interpretation).] | [In class lab exercises. Structured homework to consult model.] | [Individual and group homework and quizzes] |
| [Computer and Data Literacy: Students will learn various computational tools and when to use which ones. In this course, data literacy will focus on understanding what data is appropriate in various situations and contexts and how to analyze and interpret results. The Python programming language and tools will be used throughout the course. ] | [Visualize and clean data in in project for machine learning.] | [In class lab exercises. Structured homework using Python.] |
| [ML Algorithms: Students will learn to use core ML algorithms including those relating to supervised learning and unsupervised learning.] |
[Build models and classifiers ] |
[Individual and group homework and quizzes.] |
|
Responsible ML: Students will be introduced to important ethical issues surrounding ML and AI more generally including issues of bias, fairness, transparency, privacy, accountability, and ethics. |
Class discussion and case studies. |
Case study presentation. |
|
Effective Communication: Students will practice effective communication of methodological approaches and results of empirical analyses. |
Formulate and conduct small research projects. |
Class project. |
| Dates | Lesson Topic | Assignment | Assessment |
|---|---|---|---|
|
Week 1 |
[Introduction] | [Chapter 1 / Sections 1.1 to 1.5] | [
The homework (HW) will be assessed based on both completeness and correctness. Partial credit will be given based on evidence for students developing an understanding of the chapter concepts. ] |
| Week 2 [dates] |
[Python-1] | [Chapter 2 / Sections 2.1 to 2.6] | [The homework (HW) will be assessed based on both completeness and correctness. Partial credit will be given based on evidence for students developing an understanding of the chapter concepts. ] |
| Week 3 [dates] |
[Python-2] | [Chapter 2 / Sections 2.7 to 2.8] | [The homework (HW) will be assessed based on both completeness and correctness. Partial credit will be given based on evidence for students developing an understanding of the chapter concepts. ] |
| Week 4 [dates] |
[ML Methods for Linear Regression Models
|
[Chapter 4 / Appendix A, Sections 4.1 to 4.4 / Appendix B | [The homework (HW) will be assessed based on both completeness and correctness. Partial credit will be given based on evidence for students developing an understanding of the chapter concepts. ] |
| Week 5 [dates] |
ML Methods for Linear Classification
|
Chapter 5 / Section 5.1 to 5.3 | [The homework (HW) will be assessed based on both completeness and correctness. Partial credit will be given based on evidence for students developing an understanding of the chapter concepts. ] |
| Week 6 [dates] |
[ML Methods for Linear Classification
|
[Chapter 5 / Section 5.1 to 5.3] | [The homework (HW) will be assessed based on both completeness and correctness. Partial credit will be given based on evidence for students developing an understanding of the chapter concepts. ] |
| Week 7 [dates] |
[Non-Parametric Machine Learning. Methods for both Regression and Classification
|
[Chapter 5 / Section 5.4 ] |
[The homework (HW) will be assessed based on both completeness and correctness. Partial credit will be given based on evidence for students developing an understanding of the chapter concepts. ] |
| Week 8 [dates] |
Non-Parametric Machine Learning. Methods for both Regression and Classification
|
[]Chapter 6 / Section 6.2] | [The homework (HW) will be assessed based on both completeness and correctness. Partial credit will be given based on evidence for students developing an understanding of the chapter concepts. ] |
| Week 9 [dates] |
[ Clustering
|
Chapter 7 / Sections 7.1 to 7.3 | [The homework (HW) will be assessed based on both completeness and correctness. Partial credit will be given based on evidence for students developing an understanding of the chapter concepts. ] |
| Week 10 [dates] |
Dimensionality Reduction
|
Chapter 8 / Sections 8.7 to 8.4 | [The homework (HW) will be assessed based on both completeness and correctness. Partial credit will be given based on evidence for students developing an understanding of the chapter concepts. ] |
| Week 11 [dates] |
Dimensionality Reduction
|
Chapter 8 / Sections 8.7 to 8.4 | [The homework (HW) will be assessed based on both completeness and correctness. Partial credit will be given based on evidence for students developing an understanding of the chapter concepts. ] |
| Week 12 [dates] |
Neural networks (Brief Introduction)
|
[Chapter 9 / Section 9.1 to 9.2] | [The homework (HW) will be assessed based on both completeness and correctness. Partial credit will be given based on evidence for students developing an understanding of the chapter concepts.] |
| Week 13 [dates] |
Neural networks (Brief Introduction)
|
[Chapter 9 / Section 9.1 to 9.2] | [The homework (HW) will be assessed based on both completeness and correctness. Partial credit will be given based on evidence for students developing an understanding of the chapter concepts.] |
| Week 14 [dates] |
Responsible AI
|
Chapter 13 / Sections 13.1 to 13.6] | [The homework (HW) will be assessed based on both completeness and correctness. Partial credit will be given based on evidence for students developing an understanding of the chapter concepts.] |
| Week 15 [dates] |
Responsible AI
|
[Chapter 13 / Sections 13.1 to 13.6] | [The homework (HW) will be assessed based on both completeness and correctness. Partial credit will be given based on evidence for students developing an understanding of the chapter concepts.] |
Expectations for Student Effort
[Describe how much time students should expect to invest in the course each week. Graduate courses should state: "For each hour of lecture equivalent, students should expect to have a minimum of two hours of work outside of class." Note that Global campus courses will automatically include credit hour equivalents in the syllabus.] [As a general rule, you should expect to spend approximately 3 hours per week for each credit hour of a class. Since this is a 3 credit hour class, you should expect to spend 9 hour per week on this class attending lectures, labs, reading material and working assignments.
Grading [add more lines if necessary]
| Type of Assignment (tests, papers, etc) | Points | Percent of Overall Grade |
|---|---|---|
| [Written Homework - 5 written problems (20 points each)] | [100 points] | [25%] |
| [Exams - 2 midterm exams(100 points each)] | [200 points] | [25% per exam] |
| [Course Project] | [100 points] | [25%] |
| Grade | Percent | Grade | Percent |
|---|---|---|---|
| A |
[100% - 93%] |
C | [73% - 76.9% ] |
| A- | [90% - 92.9%] | C- | [70% - 72.9%] |
| B+ | [87% - 89.9%] | D+ | [67% - 69.9%] |
| B | [83% - 86.9%] | D | [60% - 66.9%] |
| B- | [80% - 82.9%] | F | [Below 60%] |
| C+ | [77% - 79.9%] |
[Provide information about how grades will be rounded (eg, if 89% earns a B+ and 90% earns an A-, what grade is given to a student with an 89.5?]
Attendance and Make-Up Policy
[Provide details on how attendance affects final course grades. Indicate whether and how missed exams, laboratory sessions, etc. can be made up. Sample attendance statement: “Students should make all reasonable efforts to attend all class meetings. However, in the event a student is unable to attend a class, it is the responsibility of the student to inform the instructor as soon as possible, explain the reason for the absence (and provide documentation, if appropriate), and make up class work missed within a reasonable amount of time, if allowed. Missing class meetings may result in reducing the overall grade in the class.” ] [
Students are expected to attend and participate in class. Students are also expected to complete online work in a timely manner. In the event of a medical excuse or other serious problems, please contact the instructor at the earliest possible time. Makeups will be considered following University policies. See Office of the Registrar, Academic Regulations, #72 Class Attendance and Absences, https://registrar.wsu.edu/academic-regulations/.
Academic Integrity Statement
You are responsible for reading WSU's Academic Integrity Policy, which is based on Washington State law. If you cheat in your work in this class you will:
-[fail]
-Be reported to the Center for Community Standards
-Have the right to appeal my decision
-Not be able to drop the course of withdraw from the course until the appeals process is finished
If you have any questions about what you can and cannot do in this course, ask me.
If you want to ask for a change in my decision about academic integrity, use the form at the Center for Community Standards website. You must submit this request within 21 calendar days of the decision.