Big Data and Machine Learning

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.
• identify current real-world problems that can benefit from emerging machine learning techniques and the modern big data technologies.
• design machine learning and associated algorithms that can address one of the real-world problems that you selected for the experiment.
• develop advanced programming framework that can process advanced approaches for the real-world problem that you used for the experiment.
• Research modern machine learning techniques and compare novel algorithms to address emerging problems.

Course Grading Information:

Activity/Performance Measure	Percentage/Points
Assignments	30%
Class Tests	20%
Final Exam	30%
Research	20%

Grading Scale

Percentage	Letter Grade
93-100	A
90-92	A-
87-89	B+
83-86	B
80-82	B-
76-79	C+
70-75	C
<70	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:

Students do not need to purchase any textbooks for this course. All the learning materials and resources will be supplied by the instructor.