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CMSC498M - Foundations of Data Science (view source)
Revision as of 20:39, 4 April 2017
, 20:39, 4 April 2017Created page with "'''Course Information''' <br> Data science is a fast-expanding area that touches upon numerous disciplines. This class covers some of the mathematical and algorithmic foundati..."
'''Course Information''' <br>
Data science is a fast-expanding area that touches upon numerous disciplines. This class covers some of the mathematical and algorithmic foundations of this subject, including (but not limited to) aspects of high-dimensional geometry, best-fit subspaces and singular value decomposition, machine learning, and algorithms for massive data sets. Mathematical maturity and an interest in algorithmic and mathematical reasoning are essential prerequisites for this class. The freely-available textbook "Foundations of Data Science" by Blum, Hopcroft, and Kannan, available at http://www.cs.cornell.edu/jeh/book.pdf will be the textbook for the class.
{| style="text-align:left; width: 550PX;"
! Instructor
! Section
! Day & Time
! Location
|- style="vertical-align:top;" |
| Aravind Srinivasan || style="vertical-align:top;" | 0101 || TuTh 12:30pm - 1:45pm || style="vertical-align:top;" | [http://maps.umd.edu/map/index.html?Welcome=False&MapView=Detailed&LocationType=Building&LocationName=406 CSI 1122]
|}
''' Course Prerequisite(s) ''' <br>
Prerequisite: Minimum grade of C- in CMSC351; and permission of CMNS-Computer Science department. Or must be in the (Computer Science (Doctoral), Computer Science (Master's)) program.
''' Class Webpage ''' <br>
''' Hours Per Week ''' <br>
''' Languages Used ''' <br>
''' Recommended Prior Experience ''' <br>
''' Projects, Exams, or other Assessments ''' <br>
''' Misc Info ''' <br>
This course is in no way equivalent to CMSC320 nor will it satisfy the requirement for Data Science students.
Data science is a fast-expanding area that touches upon numerous disciplines. This class covers some of the mathematical and algorithmic foundations of this subject, including (but not limited to) aspects of high-dimensional geometry, best-fit subspaces and singular value decomposition, machine learning, and algorithms for massive data sets. Mathematical maturity and an interest in algorithmic and mathematical reasoning are essential prerequisites for this class. The freely-available textbook "Foundations of Data Science" by Blum, Hopcroft, and Kannan, available at http://www.cs.cornell.edu/jeh/book.pdf will be the textbook for the class.
{| style="text-align:left; width: 550PX;"
! Instructor
! Section
! Day & Time
! Location
|- style="vertical-align:top;" |
| Aravind Srinivasan || style="vertical-align:top;" | 0101 || TuTh 12:30pm - 1:45pm || style="vertical-align:top;" | [http://maps.umd.edu/map/index.html?Welcome=False&MapView=Detailed&LocationType=Building&LocationName=406 CSI 1122]
|}
''' Course Prerequisite(s) ''' <br>
Prerequisite: Minimum grade of C- in CMSC351; and permission of CMNS-Computer Science department. Or must be in the (Computer Science (Doctoral), Computer Science (Master's)) program.
''' Class Webpage ''' <br>
''' Hours Per Week ''' <br>
''' Languages Used ''' <br>
''' Recommended Prior Experience ''' <br>
''' Projects, Exams, or other Assessments ''' <br>
''' Misc Info ''' <br>
This course is in no way equivalent to CMSC320 nor will it satisfy the requirement for Data Science students.
