Data Mining(Spring 2020)

**Meeting Time and Location: **

12 pm -
1:15 pm ,
Sandbridge Hall 107

**Instructor: **Prof.
Daniel Barbará.

Email: dbarbara (at) gmu (dot) edu

Office:
Eng. Bldg
4420

Office hour: by appointment

**Graduate TA: TBA**

**Course Home Page**

CS484

**Overview**

This course provides an introduction
to the
fundamental concepts in Data Mining

- The ability to apply computing principles, probability and statistics relevant to the data mining discipline to analyze data.
- A thorough understanding of model programming with data mining tools, algorithms for estimation, prediction, and pattern discovery.
- The ability to analyze a problem, identifying and defining the computing requirements appropriate to its solution: data collection and preparation, functional requirements, selection of models and prediction algorithms, software, and performance evaluation.
- The ability to understand performance metrics used in the data mining field to interpret the results of applying an algorithm or model, to compare methods and to reach conclusions about data.
- The ability to communicate effectively to an audience the steps and results followed in solving a data mining problem (through a term project)

**Prerequisites: grade of C or better in CS 310 and
STAT 344,
or permission of instructor Students not satisfying the prerequisites
will be
dropped from the class. **

**Textbook**

*Data Mining: Introduction to Data Mining by Tan, Steinbach and Kumar*

**Class Attendance**

Required. Please arrive on
time. I
expect to start at 12 sharp; Please participate in class! Ask
questions if
there is something you don't understand.

**Grading Policies**

There will also be written homeworks, a
final
project, a in-class midterm exam, and a final exam. Both the final and
midterm
are open-book and open-notes. The final exam will be
**comprehensive**, i.e.,
it will cover the entire course. Missed exams must be arranged with the
instructor BEFORE the exam. Documentation of the illness (doctor's
note)
is required. No early exams will be given and make-up exams are
strongly
discouraged.

- 40% Assignments
- 30% Exams
- 30% project

**No smartphones, LAPTOPS, TABLETS, or recorders allowed in class. Lectures cannot be recorded without special permission
**

**Honor Code**

You are expected to
abide by
the honor code. All assignments and exams are individual efforts.
Please
refer to GMU Academic
Policies and
Computer
Science Department Honor Code. **Any violation of the honor
code will
result in a zero of the assignment/exam, and may result in an F for the
class.
**