EE600/CompE596: Machine Learning for Engineering [Summer 2023]

Instructor	Dr. Junfei Xie https://smile.sdsu.edu/
Office Hours	by email appointment
Office Location	SDSU Engineering Building E-403B
Contact Info.	Email: jxie4@sdsu.edu
Prerequisites	Matlab, Discrete Mathematics, Linear Algebra

**A more complete version of the syllabus can be downloaded here. 

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 or Python. Students will also explore real-life applications of machine learning and learn to solve the application problem using machine learning techniques.

 

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.

 

Required or Recommended Materials

• Lecture handouts and slides (provided in each module) 
• Bontempi, G., & Taieb, S. B. (2017), Statistical foundations of machine learning. Universite Libre de Bruxelles.
• Tan, P. N., & Steinbach, M., & Kumar, V., Introduction to Data Mining, Pearson Education, Inc (1^st or 2^nd edition).
• Mitchell, T. (1997), Machine Learning. McGraw Hill.
• Murphy, K. P. (2012). Machine learning: a probabilistic perspective. MIT Press.
• Tipping, Michael E. "Bayesian inference: An introduction to principles and practice in machine learning." Summer School on Machine Learning. Springer, Berlin, Heidelberg, 2003. 

Optional Materials

• Montgomery, D. C., & Runger. G. C. (2010), Applied statistics and probability for engineers. JohnWiley & Sons.
• Bishop, Christopher (2006). Pattern Recognition and Machine Learning. Springer-Verlag New York.
• Alpaydin, Ethem. (2014). Introduction to machine learning. MIT press (3^rd edition).

 

Grade Scale:

A = 94.0-100.0

A- = 90.0-93.9

B+ = 87.0-89.9

B = 84.0-86.9

B- = 80.0-83.9

C+ = 77.0-79.9

C = 74.0-76.9

C- = 70.0-73.9

D+ = 67.0-69.9

D = 64.0-66.9

D- = 60.0-63.9

F = 59.9 and below

I = Did Not Complete

 

Grade Distribution and General Comments:

• 2 exams: 20% + 20%  = 40%
• Discussions: 4%
• Quizzes: 6%
• Assignments: 50%

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

 

Exams:

There will be two take-home exams during the course of the semester. There will be no make-up exams, so please plan accordingly. 

 

Discussions, Quizzes, Assignments:

For each module, this course requires the students to complete a quiz. Sometimes, students are also required to join the group discussion with a topic related to the module. The quizzes are due on Sundays and the group discussions are due on Wednesdays.

This course also requires the students to complete one assignment after completing each module. The assignments for Module 4 (linear regression), Module 5 (logistic regression), and Module 6 (neural networks) are programming assignments, which require the use of Matlab.  These assignments are due on Wednesdays.

The term project is replaced with two programming assignments on Clustering and PCA, respectively. Both require the use of Matlab. Both are due on Aug. 13. 

For the other three programming assignments (linear regression, logistic regression, and neural networks), you have the opportunity to improve the code and submit it for re-evaluation (only one chance). The submission deadline for all three assignments is on Aug. 13. The final score for these assignments will be the maximum of the scores received in the two submissions. 

Late Assignment Policy:

Assignments are considered late if they are submitted after the due date and time. Late assignments are accepted but with a 10% deduction in points per day (For example, if an assignment is turned in two days after the deadline, 20% in points will be deducted).

 

General Comments:

This course is quite intense and will require quite a bit of your time. It will require extensive reading and long hours in front of the computer. Please plan your semester accordingly. 

** NOTE: Some of the documents (handouts, slides, or assignment instructions) may have accessibility issues. If you encounter such issues, please contact me by email. 

 

Pacing Guide:                       

May

Week 1: 5/22-5/26	Check Getting Started: Overview and Resources  Join the discussion on Introduce Yourself! (Due: 5/28) Complete M1: Introduction Take M1 Quiz (Due: 5/28) Join the group discussion titled "What can Machine Learning be applied for?" (Due: 5/31) Complete M2.1-M2.2 in M2: Probability Theory Review
Week 2: 5/29-6/2	Complete M2: Probability Theory Review Take M2 Quiz (Due: 6/4) Join the group discussion titled "Uncertain vs Deterministic" (Due: 6/7) Work on M2 Assignment (6/7)

June

Week 3: 6/5-6/9	Complete M3: Point Estimation Take M3 Quiz (Due: 6/11) Work on M3 Assignment (Due: 6/14)
Week 4: 6/12-6/16	Complete M4: Linear Regression Take M4 Quiz (Due: 6/18) Work on M4 Assignment (Due: 6/21)
Week 5: 6/19-6/23	Complete M5: Logistic Regression Take M5 Quiz (Due: 6/25) Work on M5 Assignment (Due: 6/28) Get connected with your Term Project teammate
Week 6: 6/26-6/30	Complete M6.1 - M6.3 in M6: Neural Networks Review M1 - M5 and prepare for the Mid-term Exam Take Mid-term Exam on 7/2

July

Week 7: 7/3-7/7	Complete M6.4 - M6.5 in M6: Neural Networks Take M6 Quiz (Due: 7/9) Work on M6 Assignment (Due: 7/12)
Week 8: 7/10-7/14	Complete M7: Bayesian Inference Take M7 Quiz (Due: 7/16) Work on M7 Assignment (Due: 7/19) Complete Mid Course Survey
Week 9: 7/17-7/21	Complete M8: Decision Trees Take M8 Quiz (Due: 7/23) Join the group discussion titled "How can a supervised learning method be applied to solve real-life problems?" (Due: 7/26) Work on M8 Assignment (Due: 7/26)
Week 10: 7/24-7/28	Complete M9: Clustering Take M9 Quiz (Due: 7/30) Work on M9 Assignment (Due: 8/2)

 

August

Week 11: 7/31-8/4	Complete M10.4 in M10: Dimensionality Reduction Take M10 Quiz (8/6) Take M10 Assignment (8/9)
Week 12: 8/7-8/11	Review M6 - M10 Join the Closing Discussion (8/9) Take Final Exam (covers M6 - M10) on 8/13 Complete Programming Assignments 4 & Programming Assignment 5 on 8/13 Revise and resubmit M4-M6 assignments for reevaluation

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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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