CSE 4309 - Machine Learning - Fall 2022
Description of Course Content:
This course offers an introduction to machine learning. Topics include naive Bayes classifiers, linear regression, linear classificiers, neural networks and backpropagation, kernel methods, decision trees, clustering, and reinforcement learning. A strong programming background is assumed, as well as familiarity with linear algebra (vector and matrix operations), and knowledge of basic probability theory and statistics.
Prerequisites: Admitted into an Engineering Professional Program. C or better in each of the following: Algorithms (CSE 3318), Probability (IE 3301), and Linear Algebra (CSE 3380 or MATH 3330).
Student Learning Outcomes: After successfully taking this course, a student should be familiar with standard approaches to machine learning, be able to discuss pros and cons of these approaches, be able to implement basic machine learning methods, and be able to apply basic machine learning methods to real world problems.
Attendance is NOT mandatory for lectures. Attendance in lectures will NOT be used in calculating the semester grade. However, students are responsible for the material covered in the lectures. Lecture recordings will be available online on Microsoft Stream for students to review at any time.
Class participation during lectures is optional, and will not be considered for the course grade. At the same time, students are highly encouraged to participate, by asking questions, as well as answering questions by the instructor. Class participation can be an important resource for students who have difficulty understanding any part of the course material.
Students are expected to be professional and civil in their language and conduct:
For any student violating this policy, the instructor reserves the right to impose any grading penalties that the instructor considers appropriate, including a failing grade for the class, regardless of any other aspects of student performance. Examples of violations include language that is vulgar, insulting, disrespectful or threatening, making noise or talking with other students during lectures, disrupting lectures in any way, or making it difficult for other students to follow lectures in any way.
- During lectures.
- During office hours.
- In any oral, written or electronic communication with the instructor and TAs.
- In assignment submissions.
The semester score will simply be the average of all assignment scores. All assignments will have equal weight.
The final semester score, calculated based on the percentages listed above, will be converted to letter grades based on the following scale:
The instructor reserves the right to lower these thresholds, based on the distribution of scores. The instructor also reserves the right to lower a student's grade as penalty for violating the requirements of professional and civil conduct, as described in the student conduct section of this syllabus.
F: below 60%.
Any request for re-grading must be made within 5 days of receipt of that grade. Re-grading can lead to a higher or lower grade, depending on grading errors that are discovered.
There will be little or no extra credit. If there are extra credit opportunities, they will be included as part of the assignments, and they will be available to all students. There will be no make-up opportunities, and there will be no way for individual students to do extra work and improve their grade at the end of the semester.
IMPORTANT: It should be clear to every student that course grades will depend EXCLUSIVELY on the above grading criteria. Students should not request nor expect any other factor to be considered in computing the course grade. For example, factors that will NOT be considered are: need of a better grade to keep financial aid, to stay in the program, to qualify for a job offer, or to graduate. Students are expected to carefully monitor their own performance throughout the semester and seek guidance from available sources (including the instructor) if they are concerned about their performance and the course grade that they will earn. However, if the assignment scores are not good enough to warrant the desired grade at the end of the semester, there will be no other recourse for improving the grade.
The university withdrawal policy will be strictly adhered to. Up to the initial withdrawal date, all students will receive a W. After that date, the grade will be determined by the student's current average, and a WF or WP assigned as appropriate.
Expectations for Out-of-Class Study
Beyond the time required to attend each class meeting, students enrolled in this course should expect to spend an additional minimum of 9 hours per week of their own time in course-related activities, including reading required materials and completing assignments. Significantly more time may be needed for people having difficulties understanding the material, having a relatively weak mathematical or programming background, or having a relatively weak background in the prerequisite materials for this course.
University Policies and Services
All students enrolled in this course are expected to adhere to the UT Arlington Honor Code:
I pledge, on my honor, to uphold UT Arlington's tradition of academic
integrity, a tradition that values hard work and honest effort in the
pursuit of academic excellence.
I promise that I will submit only work that I personally create or
contribute to group collaborations, and I will appropriately reference
any work from other sources. I will follow the highest standards of
integrity and uphold the spirit of the Honor Code.
Instructors may employ the Honor Code as they see fit in their courses,
including (but not limited to) having students acknowledge the honor
code as part of an examination or requiring students to incorporate the
honor code into any work submitted. Per UT System Regents' Rule 50101,
paragraph 2.2, suspected violations of university's standards for academic
integrity (including the Honor Code) will be referred to the Office of
Student Conduct. Violators will be disciplined in accordance with
University policy, which may result in the student's suspension or
expulsion from the University.
- Student Support Services:
UT Arlington provides a variety of resources and programs designed to
help students develop academic skills, deal with personal situations,
and better understand concepts and information related to their courses.
Resources include tutoring, major-based learning centers, developmental
education, advising and mentoring, personal counseling, and federally
funded programs. For individualized referrals, students may visit the
reception desk at University College (Ransom Hall), call the Maverick
Resource Hotline at 817-272-6107, send a message to firstname.lastname@example.org,
or view the information at www.uta.edu/resources.
The list of topics for future lectures is tentative, and will be updated as needed.
Emergency Phone Numbers: In case of an on-campus emergency, call the UT Arlington Police Department at 817-272-3003 (non-campus phone), 2-3003 (campus phone). You may also dial 911. The non-emergency number is 817-272-3381.
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