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Class Schedules

Fall 2017 Class Schedule

School of Computing & Engineer

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  • Note these abbreviations for the days of the week: M-Monday, T-Tuesday, W-Wednesday, R-Thursday, F-Friday, S-Saturday, U-Sunday
  • Building names are abbreviated and can be determined by looking at the Campus Map.
  • Sections that have not been assigned an instructor are listed with "staff."
  • Classes that have not been assigned a classroom are listed with "TBA."
  • Classes that do not have a standard meeting pattern are listed with "ARR."


Computer Sci Electrical Engr

Course Title of Course Credit Ref. No. Section Day Hour Bldg. Room Instructor Consent Required Seats Remaining Status
5110 Network Architecture I 3.0 45022 LEC 0001 TR 10:00A - 11:15A HH 00313 Choi,Baek-Young No 9 OPEN
Regular Academic
5110 Network Architecture I 3.0 45023 LEC 0002 MW 7:00P - 8:15P HH 00312 Maswood,Mirza Mohd S No 10 OPEN
Regular Academic
5590 Special Topics 3.0 48276 LEC 0001 F 3:00P - 6:00P EDUC 00115 Goudarzvand,Somaieh No 2 OPEN
Description: This course teaches students how Python (Part 1) and Deep Learning (Part 2) actually work as well as how to apply them to applications. Students will build applied programming skills using case studies from object detection, music generation, gaming, and natural language processing. The programming will be in Python and in TensorFlow. Note: Part 1 (Week 1 – 8): Teaches programming skills useful to engineers and scientists. Learn how Python is used for machine learning applications. Note: Part 1 (Week 1 – 8): Python: There are no prerequisites for prior experience with computing tools required to perform projects. (knowledge on R or MathLab would be a good plus.) Regular Academic
Topics: Python/Deep Learn for Eng & Sc
5590 Special Topics 3.0 48288 LEC 0003 S 1:15P - 4:15P FH 00557 Tripathi,Rashmi No 21 OPEN
Description: This course teaches students how Web/Cloud technologies (Part 1) and Mobile technologies (Part 2) actually work as well as how to apply them to applications. Students will build applied programming skills using case studies for web and mobile applications (using web services for recognition and analysis of image, speech, sensor, social network trends, etc.). Part 1 (Week 1 – 8): Learning Web/Cloud programming for Web application using HTML/CSS/JavaScript & MEAN Stack (MongoDB, Express.js, AngularJS, and Node.js), Visualization (D3.js) Part 2 (Week 9 – 16): Learning programming for mobile applications (Android platform): learn the foundations of mobile platform and techniques, understand how to build mobile application using knowledge APIs (speech recognition, object recogni Regular Academic
Topics: Prog. for Web/Cloud/Mobile App
5690 Advanced Special Topics 3.0 45607 LEC 0001 MW 7:00P - 8:15P FH 00560L Medhi,Deepankar No 8 OPEN
Course Description: - What is Network Analytics? - Review of Statistics and Statistical Methods - Machine Learning: Overview - Network Protocols: Revisit, and what can be measured, error in measurements - Scripting Tools review - Reading of Selected Papers on Network Analytics Prerequisites: Good background in basic statistics. Preferably, two courses in networking such as CSEE 5110, CSEE 5111, CSEE 5113, ECE 5577, ECE 477, or topics courses such as cloud computing. Students are expected to have a reasonable programming background (no restriction on language). Regular Academic
Topics: Network Analytics
5697 Directed Readings 1.0-3.0 45899 LEC 0001 ARR ARR ARR Medhi,Deepankar Yes 10 OPEN
Regular Academic
5699 Research and Dissertatio 1.0-12.0 45913 LEC 0001 ARR ARR ARR Medhi,Deepankar Yes 6 OPEN
Regular Academic