CompE596: Machine Learning for Engineering [Fall 2022]

Instructor	Dr. Junfei Xie https://smile.sdsu.edu/ (Links to an external site.)
Office Hours	Tuesdays & Thursdays: 9am-10am  (either by visiting my office at E-403B or via Zoom at https://SDSU.zoom.us/j/87402863170  or by appointment)
Contact Info.	Email: jxie4@sdsu.edu
Prerequisites	Matlab, Discrete Mathematics, Linear Algebra
When/Where	TTh: 17:30 - 18:45 at PSFA-310
TA Info	Anuja Ravindra Gagangras (Email: agagangras9587@sdsu.edu)

* For any questions related to programming assignments, contact the TA. For other questions, contact the instructor. 

 

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. 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, 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

• Lecture handouts/slides (provided)
• 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).

 

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 6 individual assignments. The assignments 2-6 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: 5%
• Assignments: 35% 
• 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 

8/23	T	Discuss syllabus
8/25	Th	Introduction
8/30	T	Probability Theory Review

 

Tentative Lecture Schedule for September

9/1	Th	Probability Theory Review 9/2 is the last day to drop class!
9/6	T	Probability Theory Review
9/8	Th	Probability Theory Review
9/13	T	Point estimation
9/15	Th	Point estimation
9/20	T	Point estimation / Linear regression
9/22	Th	Linear regression   Assignment 1 (Probability & Point Estimation) due
9/27	T	Linear regression
9/29	Th	Logistic regression

 

Tentative Lecture Schedule for October

10/4	T	Logistic regression
10/6	Th	Neural Networks  Assignment 2 (Linear Regression) due
10/11	T	Neural Networks
10/13	Th	Neural Networks
10/18	T	Neural Networks Assignment 3 (Logistic Regression) due
10/20	Th	Exam 1 Review
10/25	T	Exam 1 (Cover everything before Neural Network)
10/27	Th	Dimension Reduction

 

Tentative Lecture Schedule for November

11/1	T	Dimension Reduction
11/3	Th	Dimension Reduction
11/8	T	Decision trees Assignment 4 (Neural Networks) due
11/10	Th	Decision trees
11/15	T	Decision trees
11/17	Th	Clustering
11/22	T	Clustering Assignment 5 (Dimension Reduction) due
11/24	Th	Thanksgiving Holiday, No class!
11/29	T	Bayesian inference

 

Tentative Lecture Schedule for December

12/1	Th	Bayesian inference
12/6	T	Exam 2 Review Assignment 6 (Clustering) due
12/8	Th	Exam 2 (Cover everything after Logistic Regression)
12/18	Th	Term Project Due

 

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