Course Syllabus

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CompE596: Machine Learning for Engineering [Fall 2021]

Instructor

Dr. Junfei Xie

https://smile.sdsu.edu/ (Links to an external site.)

Office Hours

Tuesdays & Thursdays: 9:30am-10:30am  (either by visiting my office at E-403B or via Zoom at

https://SDSU.zoom.us/j/81336307012 (Links to an external site.)

) or by appointment

Contact Info.

Email: jxie4@sdsu.edu

Prerequisites

Matlab, Discrete Mathematics, Linear Algebra

When/Where

TTh: 17:30 - 18:45 at M120

TA Info

 Roberto Vasquez (Email: rvasquez9614@sdsu.edu)

 

Effective Fall 2021, students who register for face-to-face classes are expected to attend as indicated in the course schedule. Faculty teaching face-to-face courses will not be required to create a new, alternative on-line class as an accommodation for any student. 

Students with medical conditions that would present a COVID-related risk in a face-to-face instructional setting should contact the Student Ability Success Center (https://sdsu.edu/sasc) to begin the process of getting support. Students who do not adhere to the Covid19 Student Policies or the directives of their faculty will be directed to leave the classroom and will be referred to the Center for Student Rights and Responsibilities.  

Do not come to campus if you do not feel well. Remain home and monitor your symptoms and seek medical attention as needed. 

 

CAMPUS VACCINATION POLICY：

On July 27, 2021, the California State University (CSU) system announced that students, faculty, and staff, including auxiliary employees will need to be immunized against SARS-CoV-2, the virus that causes COVID-19, with a vaccine record on file in order to access campus this fall. As outlined by the CSU, this requirement is not contingent on the full U.S. Food and Drug Administration (FDA) approval, and therefore removes some of the earlier uncertainty regarding the policy’s effective date.

 

SDSU will continue to operate in accordance with all federal, state, and county public health  guidelines, and in compliance with CSU policies. The university will continue to prioritize the safety of students, faculty, staff, and community, while seeking to fulfill its educational mission. Visit the university’s COVID-19 website frequently, as the site is updated with current information. 

 

CAMPUS FACIAL COVERING POLICY：

SDSU's full facial covering policy is available online and may be updated again in the fall. Please refer to the embedded URL for any fall 2021 updates, as the site will be kept current. 

Through Sept. 30, the university’s facial covering policy is as follows:

Facial covering policy for those who are fully vaccinated:

Facial coverings are required in the following settings:

  • When in public-facing indoor settings.
  • When in non-public-facing indoor settings, to include research spaces, when visitors or students are present.
  • When in instructional settings, whether indoors or outdoors.

o This includes classrooms, instructional labs, spaces being actively used in an instructional capacity, and the University Library.

o Vaccinated instructional faculty, teaching assistants and interpreters can remove their facial coverings when teaching as long as students are masked in the classroom.

Facial coverings are recommended in the following settings:

  • Indoors in non-public settings when gathering for meetings and other functions with others, if no members of the public or students are present.

Facial covering policy for those who are not vaccinated:

  • Facial coverings must be worn at all times while indoors.
  • Unvaccinated individuals must wear facial coverings outdoors when unable to maintain six feet of distance from others.
  • Certain limited exceptions to wearing a facial covering will be granted to unvaccinated individuals, which are outlined in the full policy online.

Please refer to the full facial coverings policy, housed on the university’s COVID-19 site.

 

Course Description:

Machine learning, a discipline that deals with the automatic design of models from data, has been successfully used in the past few decades for data analysis, process automation, function approximation, model building, and many others. These techniques have been explored in a diversity of fields such as robotics, self-driving cars, big data, control of autonomous systems, image analysis, object recognition, data mining, business, and financial forecasting, transportation systems, antenna design, medical care systems, and many others. It is believed by many researchers that machine learning is the best way to enable human-level artificial intelligence.

 

This course provides a broad introduction on the key machine learning techniques that are frequently used in the Engineering field. The main topics to be covered include probability theory, point estimation, linear regression, logistic regression, neural networks, decision trees, clustering, Bayesian estimation, and dimension reduction. In this course, students will not only learn these machine learning techniques, but also gain practice implementing them using programming tools such as Matlab, Python or R. Students will also explore real-life applications of machine learning and learn to solve the application problem using machine learning techniques.

 

Medical-related absences:

Please note:

  • University policy instructs students to contact their professor/instructor/coach in the event they need to miss class due to an illness, injury, or emergency. All decisions about the impact of an absence, as well as any arrangements for making up work, rest with the instructors.  
  • If a student misses class because of COVID-19, either because they have been diagnosed and are quarantined or are required to isolate and would like to request a class excuse letter, the student should send an email to vpsafrontdesk@sdsu.edu to notify the university. Student Affairs and Campus Diversity will initiate the process for absent letters to be sent to course instructors, Assistant Deans, and the Provost. Medical documentation may be required prior to the letter being issued.
  • Student Health Services (SHS) does not provide medical excuses for short-term absences due to illness or injury. When a medical-related absence persists beyond five days, SHS will work with students to provide appropriate documentation. 
  • When a student is hospitalized or has a serious, ongoing illness or injury, SHS will, at the student's request and with the student’s consent, communicate with the student’s instructors via the Vice President for Student Affairs and Campus Diversity and may communicate with the student’s Assistant Dean and/or the Student Ability Success Center.  

 

Course Learning Objectives:

Following this course, students will be able to:

  • Use different estimation approaches including least squares, maximum likelihood, and maximum a posterior to estimate unknown parameters in a function or model.
  • Differentiate supervised and unsupervised learning problems and choose proper approaches to solve these problems.
  • Construct and implement linear regression, logistic regression, neural network models from real-life application datasets programmatically using Matlab or Python, and use the models to perform predictions.
  • Construct decision trees using different methods for different types of data.
  • Identify suitable clustering techniques for different data types, use these techniques to perform clustering, and validate the clustering results using different methods.
  • Explain different dimension reduction methods and apply principal component analysis to reduce the dimension of a dataset.
  • Identify and formulate a real-life application problem, develop and implement proper machine learning algorithms to solve the problem, assess the performance of the proposed algorithm, report and present the results.

 

Recommended Materials

Optional Materials

 

Exams:

There will be two exams during the course of the semester. The grading will be relative to the performance of the entire class.

 

Quizzes, Assignments & Term Project:

This course requires the students to complete one quiz after completing each module. 

This course also requires the students to complete 7 individual assignments. The assignments 3-5 are programming assignments, which require the use of Matlab. 

Additionally, students will be required to complete a term project. Students will form teams (up to two students in a team) to identify an application domain and associated dataset, and perform machine learning tasks. For assessment, each team is required to submit a final report, a recorded presentation of the project and a demo of the codes. 

 

Grade Distribution and General Comments:

  • 2 exams: 20% + 20%  = 40%
  • Quizzes: 10%
  • Assignments: 30% 
  • Term Project: 20% 

This course requires a LOT of reading and will require some research on your own for the term project.

 

Late Assignment & Makeup Exams Policy

Assignments are considered late if they are submitted after the due date and time. I will not accept late assignments so please make sure you turn in your assignments on time. 

 

Makeup exams will not be given under normal circumstances. If you notify me immediately that serious, unavoidable, documentable (e.g., with a letter from your doctor) circumstances have arisen, I will discuss options for replacing the missing grade. 

 

Pacing Guide:                       

Tentative Lecture Schedule for August 

8/24

T

Discuss syllabus, Introduction

8/26

Th

Probability Theory Review

8/31

T

Probability Theory Review

 

Tentative Lecture Schedule for September

9/2

Th

Probability Theory Review 9/3 is the last day to drop class!

9/7

T

Probability Theory Review

9/9

Th

Point estimation

9/14

T

Point estimation  Assignment 1 (Probability Theory) due on 9/14

9/16

Th

Linear regression 

9/21

T

Linear regression

9/23

Th

Linear regression  Assignment 2 (Point Estimation) due on 9/23

9/28

T

Logistic regression

9/30

Th

Logistic regression

 

Tentative Lecture Schedule for October

10/5

T

Neural Networks

10/7

Th

Neural Networks  Assignment 3 (Linear Regression) due on 10/3

10/12

T

Neural Networks

10/14

Th

Neural Networks

10/19

T

Neural Networks Assignment 4 (Logistic Regression) due on 10/19

10/21

Th

Mid-term Review

10/26

T

Dimension Reduction 

10/28

Th

Mid-term Exam (Cover everything before Neural Network)

 

Tentative Lecture Schedule for November

11/2

T

Dimension Reduction 

11/4

Th

Dimension Reduction

11/9

T

Decision trees Assignment 5 (Neural Networks) due on 11/9

11/11

Th

Veterans Day, No class.

11/16

T

Decision trees

11/18

Th

Decision trees & Clustering

11/23

T

Clustering Assignment 6 (Dimension Reduction & Decision Trees) due on 11/23

11/25

Th

Thanksgiving Holiday, No class.

11/30

T

Bayesian inference

 

Tentative Lecture Schedule for December

12/2

Th

Bayesian inference

12/7

T

Final Exam Review Assignment 7 (Clustering & Bayesian Inference) due on 12/7

12/9

Th

Final Exam (Cover everything after Logistic Regression)

12/16

Th

Term Project Due on 12/16

 

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This course will use the Canvas Learning Management System instead of Blackboard. To access your course log in at canvas.sdsu.edu, and sign in using your SDSUid.

Note: You are responsible for adjusting your notification settings in such a way that you receive ALL announcements regarding this class. All Canvas email notifications will be delivered to your SDSU email address. You can add additional email addresses and sign up for text/mobile app notifications via the settings in your Canvas Profile, and then adjust your notifications in the Notifications Tab. Canvas notifications are system wide and cannot be adjusted by course. Click here to view a step-by-step guide to add additional notification and contact methods. 

If you have technical issues with Canvas, please contact the SDSU Canvas 24/7 support line at 619.483.0632.

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Course Summary:

Date Details Due