Instructor: Dr. Angelina A Tzacheva, Department of Computer Science, College of Computing and Informatics,
OfficeHours: Tuesday 4pm to 6pm Skype, Install Skype from Skype.com, call the userID  angelina.tzacheva during office hour time - for Live Video Conferencing, EMail: aatzache@uncc.edu

Teaching Assitants:
1. Akshaya Easwaran, OfficeHours: Monday, Tuesday 10am to 11:30am, by Live Web Conference using Skype, SkypeID:akshuhrsh,  Email: aeaswar1@uncc.edu,
Webex Link - https://uncc.webex.com/uncc/j.php?MTID=m2fc4b1e4225771f398243de8ee43e5ec Meeting Number: 613 789 144

Prerequisites: ITCS 3153 - Introduction to Artificial Intelligence, STAT 2122 - Introduction to Probablity and Statistics

Textbook:
1. " Introduction to Machine Learning " , Second Edition, Ethem ALPAYDIN, The MIT Press, 2010:  ISBN-10: 0-262-01243-X, ISBN-13: 978-0-262-01243-0
2.  "Introduction to Data Mining" by Pang-Ning Tan, Michael Steinbauch, and Vipin Kumar. Addison Wesley, 2005. ISBN: 0321321367

Course Outline:
- Knowledge discovery process
- Types of Data, Pre-processing, Distance Measures
- Association rules discovery methods
- Discretization algorithms
- Decision Trees
- Classification methods
- KNearest Neighbor
- Artificial Neural Networks
- Regression
- Clustering Analysis
- RSES, LERS, WEKA, ORANGE
- Hadoop, MapReduce, and distributed data mining
- Application is specific domain (health, financial, education, music)


Instructional Method: 
This is an Online course which includes Video Lectures, Reading Assignments, Exercises, GroupActivites, and a Group Project.
Lectures Notes, Videos, and Reading Assignments are posted in the syllabus table below, as well as on Canvas. Please download and read each lecture material, and view each Video on the specified day.
All material by date is listed, including preparation for the exams with sample questions. The Exams are open-book / open-notes. The textbook is necessary, as exam questions are based on lecture notes AND on the text, and Exercises are assigned from the textbook.

Credit Hours: This is a 3 credit hour course.
This course is designed to require about 10 hours per week - for readings, exams, exercises, video cases, and group project work.
The material is technical, and requires dedication of time to comprehend.  To complete course successfully, Please   do not plan on   cramming  all lectures the day before the exam. Designate 3 hours every lecture day for reading the given lecture, and book chapter. Designate additional 4 hours per week for Exercises, videocase assignments, and Group meetings / activites. You can meet with your Group Members ONLINE through video conferencing - via Skype , GoogleHangout , or meet in person if desired. Students are expected to communicate and meet with their group members in order to complete the project successfuly.
Exercises are assigned after each chapter. The Exercises are due on Canvas on the dates they are assigned. Exercises are *not accepted* through e-mail. Late Exercises are not accepted.

Grading:
The final course grade is determined on the following weights:
Exercises   20%
GroupActivities 15%
Midterm Exam   20%
Group Project   25%
Final Exam   20%

Gradig scale:
A   90% - 100%
B   80% - 89%
C   70% - 79%
D   60% - 69%
F   less than 60%
X   academic dishonesty

Academic Integrity and Honesty:
Students are required to read and abide by the Code of Student Academic Integrity availbe from Dean of Students Office. This code forbids cheating, fabrication or falsification of information, multiple submissions of academic work, plagiarism (including viewing others work without instructor permission), abuse of academic materials, and complicity of academic dishonesty. Violations of the Code of Student Academic Integrity, including plagiarism, result in disciplinary action as provided by the Code.

Civility:
We are concerned with a positive learning experience. This course strives to create an inclusive academic climate in which the dignity of all individuals is respected and maintained. We value diversity that is beneficial to both employers and societey at large. Students are encouraged to actively and appropriately share their views in class discussions.

Inclement Weather:
University Policy Statement #13 states the University is open unless the Chancellor announces that the University is closed.  The inclement weather hotline number to call is 704-687-1900. In the event of inclement weather, check your e-mail, and Canvas. The instructor will post a message on Canvas, and through e-mail. The instructor will use their best judgment as to whether class should be held.

Disability:
UNC Charlotte is committed to access to education. If you have a disability and need academic accommodations, please provide a letter of accommodation from Disability Services early in the semester. For more information on accommodations, contact the Office of Disability Services at 704-687-0040 or visit their office in Fretwell 230.

Withdrawal:
The University policy on Course Withdrawal allows students a limited number of opportunities available to withdraw from courses. There are financial and academic consequences that may result from course withdrawal. If a student is concerned about his / her ability to succeed in this course it is imporant to make an appointment to speak with the instructor as soon as possible.

Syllabus Revision:
The instructor may modify the class schedule and syllabus during the course of the semester. For example - additional educational vidoes may be posted. Same changed will appear on Canvas. Students are responsible for refreshing their syllabus once per week.

E-Mail Communication:
Students are responsible for *all* announcements made in class and on the class online resources. Students should check the online class resources throughout the semester. The Instructor and Teaching Assistants send occasional e-mails with important information. We send this information to the student's UNCCharlotte e-mail address listed on Banner system. If a student is not checking his / her UNCCharlotte e-mail address ( ex. userName@uncc.edu ) please be sure to access this e-mail and check it regularly during this course.

Class Expectation:
By attending class beyond the first week, students agree to follow the framework and rules related to this course as described above.