|
|
Role |
Office
hours |
Office
location |
E-mail
address |
Phone |
|
|
Jicheng
Fu |
Professor |
Monday, Wednesday, and Friday:
1:00 – 2:15pm |
STEM237 |
974-5704 |
Lecture Time & Location: Monday, Wednesday, and Friday 9:00 am - 9:50 am, MCS115
Course Web Page: https://cs2.uco.edu/~fu/CMSC4143/index.html
Getting Help
General questions about the homework assignments
should be directed to the instructor at the above e-mail address. You are
encouraged to use the eLearning discussion group for the course on D2L.
Therefore, you should check the discussion group whenever you have a question
about the assignment as someone else may have already asked it and
received an answer.
Course Description
Machine learning is concerned with the question of how to construct computer programs that automatically improve their performance through experience. This course provides an in-depth study of modern algorithms for machine learning, including supervised learning, unsupervised learning, and reinforcement learning. Python is the programming language used for implementing course projects.
Text
Machine Learning by Tom Mitchell. McGraw-Hill Education ISBN-10:
0071154671
Reinforcement
Learning: An Introduction, Second Edition by Richard S. Sutton and Andrew G. Barto,
Edition: 2nd ISBN: 9780262039246
Prerequisite
CMSC 3613 Data Structures and Algorithms
Grading
Homework assignments (30% of the course grade)
There will be a written as well as a programming assignment for each topic.
Since we will be using eLearning (D2L) for submission and grading, you must
upload an electronic copy of your assignment by the due date. For written
assignments, if you choose not to typewrite your assignment, you will need to
scan and upload your submission.
Library assignment (5% of the course grade)
Using UCO library and database search
facilities, locate, access, and read one article published within five years by
the Association for Computing Machinery (ACM) or by the Institute of Electrical
and Electronics Engineers (IEEE). The article must be relevant to
topics of Machine Learning.
Midterm exam (30% of the course grade)
There will be one in-class midterm exam. The midterm exam is tentatively scheduled on Wednesday, October
18.
Final exam (30% of the course grade)
There will be a comprehensive final exam. Exam date: December 13.
Class participation (5% of the course grade)
·
A student is allowed to miss two complete classes without penalty. After
that, unexcused absence will be counted.
·
For IVE students:
o Please use your real name to join our online sessions
o If you cannot make the class, you
must watch the course video within 48
hours after the class. Please update your attendance records in D2L
accordingly.
·
To improve the learning quality, students are encouraged to actively ask
questions, answer questions, and get involved in discussions. Attitude is everything.
Course Policies
Collaboration policy
The written assignments are individual and programming assignments are either individual- or group-based. Each group consists of at most two students.
Academic integrity
policy
You are expected to maintain the utmost level of academic integrity in the course, in accordance with the academic integrity policy of the University of Central Oklahoma. In particular, (a) it is your responsibility to protect your work from unauthorized access, and (b) the work you submit is expected to be your own. Academic dishonesty has no place in a university or anywhere else: it wastes our time and yours, and it is unfair to everyone else. Any violation of this code will be penalized, as we take this issue very seriously. Any student observed cheating will receive a grade of zero on the exam or assignment, and the appropriate college administrative personnel contacted. A second offense will result in dismissal from the class with a grade of F.
Late assignment policy
Barring extenuating circumstances, all assignments must be turned
in on the date specified. You will be given three ''free'' late days, with
the restriction that no more than two free late days can be spent on each
homework assignment. If it is a group assignment, students of the entire group
will be considered using the free late days. After you use up the free late
days, the late submissions will be penalized as follows. Assignments
turned in within 24 hours of the due date will receive 90% of its score.
Assignments turned in within 48 hours of the due date will receive 70% of its
score. Assignments more than 48 hours late will not be accepted.
Regrade policy
The professor will grade your work carefully.
However, questions about grading do occasionally arise. If so, first read the
solutions. If questions persist, please see me of that problem (come to office
hours or schedule an appointment). In the interests of
smooth administration and to encourage you to look at your graded
work soon after it is returned, regrade requests must be made
within two weeks of when the work was returned. We reserve the
rights to make regrade decisions "off-line" (i.e., not
immediately at the time requested).
COURSE VIDEOS
Due to limitations on
the disclosure of personally identifiable information under certain federal
privacy laws, students are not permitted to record class sessions or allow
non-students to view online class sessions. Sharing links of class videos or
add class videos to a public list is also prohibited. Students registered
with the UCO Office of Disability Support Services may request accommodation of
the prohibition and must present a copy of the DSS letter to the instructor.
Title IX
The University of
Central Oklahoma complies with Section 504 of the Rehabilitation Act of 1973
and the Americans with Disabilities Act of 1990. Students with disabilities who
need special accommodations must make their requests by contacting Disability
Support Services, at (405) 974-2516. The DSS Office is located in the
Nigh University Center, Room 305. Students should also notify the instructor of
special accommodation needs as soon as possible. Per Title IX of the Education
Amendments of 1972 (“Title IX”), pregnant and parenting students may request
adjustments by contacting the Title IX Coordinator, at (405) 974-3377 or TitleIX@uco.edu.
The Title IX Office is located in the Lillard Administration Building, Room
114D.
Important Dates
|
Week |
Dates |
Monday |
Wednesday |
Friday |
|
1 |
08/21-08/25 |
Introduction |
Univariate Linear Regression (1) |
Univariate Linear Regression (2) Programming assignment: Univariate Linear Regression |
|
2 |
08/28-09/01 |
Univariate Linear Regression (3) |
Review: Linear
Algebra & Vectorization |
Multivariate Linear Regression – (1) Programming assignment: Multivariate Linear
Regression |
|
3 |
09/04-09/08 |
Labor Day |
Multivariate Linear Regression (2) Programming & Written assignment:
logistic regression |
Logistic Regression (1) |
|
4 |
09/11-09/15 |
Logistic
Regression (2) |
Logistic Regression (3) |
Logistic Regression (4) |
|
5 |
09/18-09/22 |
Neural Network (1) Programming & Written assignment:
Artificial neural network |
Neural
Network (2) |
Neural Network (3) |
|
6 |
09/25-09/29 |
Neural Network (4) |
Neural Network (5) |
Neural Network (6) |
|
7 |
10/02-10/06 |
Optimization & Evaluation |
Decision Tree (1) Programming & Written assignment: Decision tree |
Decision Tree (2) |
|
8 |
10/09-10/13 |
Decision Tree (3) + Review |
Decision Tree (4) Assignment: Library research |
Fall Break |
|
9 |
10/16-10/20 |
Decision Tree (5) |
Midterm |
Instance-Based Learning (1) |
|
10 |
10/23-10/27 |
Instance-Based Learning (2) Written assignment: Instance-based learning
& clustering |
Clustering
(1) |
Clustering
(2) |
|
11 |
10/30-11/03 |
Clustering (3) |
Markov Decision Processes (1) Programming assignment: Markov decision process |
Markov
Decision Processes (2) |
|
12 |
11/06-11/10 |
Markov
Decision Processes (3) |
Policies and Value Functions |
Bellman
Equations |
|
13 |
11/13-11/17 |
Optimization Written assignment: Reinforcement learning |
Policy Evaluation |
Policy Iteration |
|
14 |
11/20-11/24 |
Generalized Policy Iteration |
Thanksgiving |
|
|
15 |
11/27-12/01 |
Q-Learning (1) Bonus programming assignment |
Q-Learning (2) |
Bayesian Learning (1) |
|
16 |
Bayesian
Learning (2) |
Review and Discussion |
Bonus project demonstration |
|
|
17 |
12/11-12/15 |
Final exam |
|
|
This schedule (including exam dates) is subject
to change. You are responsible for attending class and staying aware of
announced schedule updates.