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ITWS Concentration: Machine and Computational Learning
(Science)
For those entering the program Fall 2011 semester & beyond
Contact Person: Mark Goldberg and Malik Magdon-Ismail
Description:
This concentration of study prepares a student to work in the areas of Information Technology & Web Science that involve the development of intelligent systems for complex computational tasks in areas such as bioinformatics, voice and image recognition, and Internet development.
The knowledge of the methods of machine and computational learning enables the student not only to identify situations where intelligent algorithms would amplify performance, but also to develop such algorithms.
Required Courses:
| Semester I |
Semester II |
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ITWS-1100 Introduction to Information Technology and Web Science
CSCI-1100 Computer Science I
Physical Science Elective (PHYS-XXXX)
MATH-1010 Calculus I |
ITWS-1220 IT and Society
CSCI-1200 Data Structures
MATH-2800 Intro to Discrete Structures
HASS Elective (2)
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| Semester III |
Semester IV |
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One of:
- ITWS-2961 Creativity and IT
- ITWS-496X IT for Arts and Performance
CSCI-2300 Introduction to Algorithms
Machine Learning Elective (1)
ITWS-2110 Web Systems Development
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ITWS-4200 Web Science
ITWS-2210 Intro to Human Computer Interaction
CSCI-2500 Computer Organization
Free Elective |
| Semester V |
Semester VI |
|
ITWS-4310 Managing IT Resources
Life Science Elective (BIOL-XXXX)
Machine Learning Elective (1)
HASS Elective (2) |
ITWS Elective (one of):
- CSCI-4380 Database Systems
- MGMT-4170 Data Resource Management
CSCI-4150 Intro to Artificial Intelligence
Machine Learning Elective (1)
HASS Elective (2) |
| Semester VII |
Semester VIII |
One of: (3)
- ITWS-4100 Information Technology and Web Science Capstone (Professional Track)
- ITWS-4990 Senior Thesis (Research Track)
Machine Learning Elective (1)
Machine Learning Elective (1)
Free Elective |
ISYE-4810 Computational Intelligence
Machine Learning Elective (1)
HASS Elective (2)
Free Elective
ITWS-4990 Senior Thesis (Research Track Only) |
Students must satisfy an 8-credit communication requirement. See your advisor for details.
(1) Machine Learning Electives may be chosen from among:
CSCI-2400 Models of Computation
CSCI-4020 Computer Algorithms
CSCI-4380 Database Systems
CSCI-4390 Database Mining
CSCI-4999 Machine Learning in Bioinformatics
ECSE-4540 Introduction to Image Processing
PHIL-2140 Introduction to Logic
PHIL-4260 Philosophy of Artificial Intelligence
PHIL-4380 Philosophy of Mathematics
PHIL-4420 Computability and Logic
PHIL-4440 Knowledge and Rationality
The following graduate courses can be used as Machine Learning Electives with the permission of either Prof. Goldberg or Prof. Magdon-Ismail: CISH-6150 AI and Heuristics, ECSE-6610 Pattern Recognition, and ECSE-6720 Neural Network Computing.
(2) See HASS requirements
(3) Co-terminal students would replace ITWS-4100 Information Technology and Web Science Capstone with ITWS-4980 Special Projects course which will be the culminating experience. |
