CMSC4143/5143 Algorithms for Machine Learning

  

Role

Office hours

Office location

E-mail address

Phone

Jicheng Fu

Professor

4:00 – 5:30pm on Monday, Wednesday, and Friday

STEM237

jfu@uco.edu

974-5704

Lecture Time & Location: Monday, Wednesday, and Friday 11:00 am - 11:50 am, MCS113

Teaching Assistant: Kofi Gyan kapau@uco.edu

Course Web Page: https://cs2.uco.edu/~fu/CMSC4143/index.htm


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 13.

 

Final exam (30% of the course grade) 
There will be a comprehensive final exam. Exam date: December 15.

 

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, please use your real name to join our online sessions

·       If you cannot make the class, you must watch the 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.

 


University Policies

If you have tested positive for COVID-19 or have had direct exposure to someone with COVID-19, file a report with the COVID-19 Response Team at https://uco.co1.qualtrics.com/jfe/form/SV_39Omw83BStDpw1L

 

Please also refer to student information sheet for more information (attached and online: https://www.uco.edu/academic-affairs/files/student-info-sheet.pdf)

Course Policies

Collaboration policy 
The written assignments are individual and programming assignments are 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 and TA will grade your work carefully. However, questions about grading do occasionally arise. If so, first read the solutions. If questions persist, please see me or TA 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

08/23-08/27

Introduction

Univariate Linear Regression (1)

Programming assignment: Univariate Linear Regression

Univariate Linear Regression (2)

 

2

08/30-09/03

Univariate Linear Regression (3)

Review: Linear Algebra & Vectorization

Multivariate Linear Regression – (1)

Programming assignment: Multivariate Linear Regression

3

09/06-09/10

Labor Day

Multivariate Linear Regression (2)

Programming & Written assignment: logistic regression

Logistic Regression (1)

4

09/13-09/17

Logistic Regression (2)

Logistic Regression (3)

Logistic Regression (4)

5

09/20-09/24

Neural Network (1)

Programming & Written assignment: Artificial neural network

Neural Network (2)

Neural Network (3)

6

09/27-10/01

Neural Network (4)

Neural Network (5)

Neural Network (6)

7

10/04-10/08

Optimization & Evaluation

Decision Tree (1)

Programming & Written assignment: Decision tree

Decision Tree (2)

8

10/11-10/15

Decision Tree (3) + Review

Midterm

Fall Break

9

10/18-10/22

Decision Tree (4)

Assignment: Library research

Decision Tree (5)

Instance-Based Learning (1)

10

10/25-10/29

Instance-Based Learning (2)

Written assignment: Instance-based learning & clustering

Clustering (1)

Clustering (2)

11

11/01-11/05

Clustering (3)

Markov Decision Processes (1)

Programming assignment: Markov decision process

Markov Decision Processes (2)

12

11/08-11/12

Markov Decision Processes (3)

Policies and Value Functions

Bellman Equations

13

11/15-11/19

Optimization

Written assignment: Reinforcement learning

Policy Evaluation

Policy Iteration

14

11/22-11/26

Generalized Policy Iteration

Thanksgiving

15

11/29-12/03

Q-Learning (1)

Bonus programming assignment

Q-Learning (2)

Bayesian Learning (1)

16

12/06-12/10

Bayesian Learning (2)

Review and Discussion

Bonus project demonstration

17

12/13-12/17

Final exam

 

 

This schedule (including exam dates) is subject to change. You are responsible for attending class and staying aware of announced schedule updates.