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Fall 2019
Aug 25, 2019
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CS 37300 - Data Mining And Machine Learning
Credit Hours: 3.00. This course will introduce students to the field of data mining and machine learning, which sits at the interface between statistics and computer science. Data mining and machine learning focuses on developing algorithms to automatically discover patterns and learn models of large datasets. This course introduces students to the process and main techniques in data mining and machine learning, including exploratory data analysis, predictive modeling, descriptive modeling, and evaluation. Typically offered Fall Spring.
3.000 Credit hours

Syllabus Available
Levels: Graduate, Professional, Undergraduate
Schedule Types: Distance Learning, Lecture

Offered By: College of Science
Department: Computer Science

Course Attributes:
Upper Division

May be offered at any of the following campuses:     
      West Lafayette

Learning Outcomes: 1. Identify key elements of data mining and machine learning algorithms. 2. Understand how algorithmic elements interact to impact performance. 3. Understand how to choose algorithms for different analysis tasks. 4. Analyze data in both an exploratory and targeted manner. 5. Implement and apply basic algorithms for supervised and unsupervised learning. 6. Accurately evaluate the performance of algorithms, as well as formulate and test hypotheses.


Restrictions:
Must be enrolled in one of the following Majors:     
      Computer Science
      Computer Science Honors
      Data Science
      Data Science

Prerequisites:
Undergraduate level CS 18200 Minimum Grade of C and Undergraduate level CS 25100 Minimum Grade of C and (Undergraduate level STAT 35000 Minimum Grade of C [may be taken concurrently] or Undergraduate level STAT 35500 Minimum Grade of C [may be taken concurrently] or Undergraduate level STAT 51100 Minimum Grade of C [may be taken concurrently])

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