Instructor Information
Instructor: Mr. Xinjie Lan
Email: lxjbit@udel.edu
Office: 153 Evans Hall
Office hours: Tuesday/Thursday 10:30 AM - 12:00 AM
Embedded Files
Course description
The course provides an introduction to the classical models, algorithm, and theory of machine learning. The course topics tentatively include: linear regression, logistic regression, support vector machine, neural network, clustering, and principal component analysis. The detailed syllabus is available here.
The teaching goal of the course is three-fold: (1) fostering the studying interests of machine learning; (2) establishing a solid mathematical foundation of machine learning; and (3) cultivating critical-thinking skills and practical programming skills to solve real-world problems.
Textbook
"Machine Learning, A Probabilistic Perspective", K. Murphy (2021).
Prerequisites
Mathematical background:
Probability theory
Linear algebra
Calculus
Programming background:
Python (recommend)
Matlab
Grading System and Assignments
Grading
Homework assignments 15%
Computer assignments 30%
Graded Discussion 15%
Midterm Exam 20%
Final Exam 20%
Assignment Instructions
You will post your completed assignments in your personal mailbox in Canvas. I will review and assess your work, enter feedback and a grade directly in the document you have submitted, and then post the updated document in your private mailbox.
End-of-module homework and computer assignments (modules 1 through 7)
Address concepts and skills from a given module
Open book
You may collaborate with your classmates; however, you must submit your own work.
I will post a copy of your response, with feedback and a grade.
Mid-term and final examinations
Address concepts and skills from multiple modules
Open book
Must be completed individually; collaboration is not permitted
I will post a copy of your response, with feedback and a grade.
Midterm exam becomes available on Saturday April 29 at 6am (week 4), and must be submitted before April 30 at 11:59pm
Final exam becomes available on Saturday May 20 at 6am (week 7), and must be submitted before May 21 at 11:59 pm
Submitting assignments:
Examples of items you may submit in your documents include screen shots from MATLAB, typed scripts that you have created, and scanned copies of your hand-written work. Be sure to follow the instructions on the assignment pages.
The work you submit will be private. It will be available only to you, me, and course administrators.
You will receive feedback directly in the document that you submit.
For the filenames of your assignment submissions, use the format described in the assignment documents, and be sure to follow all instructions for completing the assignments.
Policy on Late Submissions and Quizzes
The regular assignment drop box closes on the submission date of an assignment. You will be able to submit your assignment to a late submission drop box. However, the late submission will result in a 50% penalty. Assignments that are more than one week late will not be graded. Exceptions may be made for valid excuses such as illness or a work crisis. The exceptions are on a case-by-case basis.
Previous Teaching Courses
2021 Spring [Instructor] Morden Machine Learning (ELEG 845)
2021 Fall [Teaching Assistant] Digital signal processing
2020 Spring [Instructor] Morden Machine Learning (ELEG 845)
2020 Fall [Teaching Assistant] Digital signal processing
2019 Spring [Teaching Assistant] Morden Machine Learning (ELEG 845)
2018 Fall [Teaching Assistant/Guest Lecturer] Microprocessor systems and programming
2017 Fall [Teaching Assistant/Guest Lecturer] Microprocessor systems and programming
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