Course Description
Big data definitions and characteristics, computing environment for big data management and processing, machine learning models and algorithms, and scaling up machine learning (high dimensionality reduction). Prerequisite: A grade of C- or better in CSC 330 and (STA 271 or STA 290), or permission of instructor.
Syllabus
Student Learning Outcomes, Goals, Objectives:
Students successfully completing this course will be able to:
- identify the characteristics of datasets and compare the trivial data and big data for various applications.
- understand machine learning techniques and computing environment suitable for real-world applications.
- solve problems with batch and online learning, and big data characteristics such as high dimensionality, dynamically growing data and particular scalability issues.
- develop scaling up machine learning techniques and related computing systems techniques and technologies.
- implement various ways of selecting suitable model parameters for different machine learning techniques.
- integrate machine learning libraries, and mathematical and statistical tools with modern technologies like Hadoop distributed file system and MapReduce programming.
Course Grading Information:
| Activity/Performance Measure | Percentage/Points |
|---|---|
| Assignments | 45% |
| Class Tests | 20% |
| Final Exam | 35% |
Grading Scale
| Percentage | Letter Grade |
|---|---|
| 93-100 | A |
| 90-92 | A- |
| 87-89 | B+ |
| 83-86 | B |
| 80-82 | B- |
| 77-79 | C+ |
| 73-76 | C |
| 70-72 | C- |
| 67-69 | D+ |
| 63-66 | D |
| 60-62 | D- |
| <59 | F |
Attendance/Participation:
Attendance/participation is not a graded component included in the student's final grade for this course.
Course Materials Purchased by the Students:
No course materials are required.
Scholarly Perspectives
This course engages diverse scholarly perspectives to develop critical thinking, analysis, and debate and inclusion of a reading does not imply endorsement.