Graduate Studies Reports Access

Graduate Course Proposal Form Submission Detail - CAP6671
Tracking Number - 2928

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Current Status: Approved by SCNS - 2014-04-30
Campus: Tampa
Submission Type: New
Course Change Information (for course changes only):
Comments: Core for MSIT, To GC; Apprd 12/10/13; To USF Sys 2/4/14, to SCNS 2/12/14. Approved eff 4/1/14. Nmbr 6082 apprd as 6671

Detail Information

  1. Date & Time Submitted: 2012-09-11
  2. Department: Deans Office
  3. College: EN
  4. Budget Account Number:
  5. Contact Person: Alfredo Weitzenfeld
  6. Phone: 8636677769
  7. Email:
  8. Prefix: CAP
  9. Number: 6671
  10. Full Title: IT Intelligent Agents
  11. Credit Hours: 3
  12. Section Type: C - Class Lecture (Primarily)
  13. Is the course title variable?: N
  14. Is a permit required for registration?: N
  15. Are the credit hours variable?: N
  16. Is this course repeatable?:
  17. If repeatable, how many times?: 0
  18. Abbreviated Title (30 characters maximum): IT Intelligent Agents
  19. Course Online?: B - Face-to-face and online (separate sections)
  20. Percentage Online: 0
  21. Grading Option: R - Regular
  22. Prerequisites: None
  23. Corequisites: None
  24. Course Description: Introduction to Intelligent Agents and its different applications. Intelligent agent technology relates to important areas that include artificial intelligence, neural networks, and expert systems. These areas will be discussed during the class.

  25. Please briefly explain why it is necessary and/or desirable to add this course:
  26. What is the need or demand for this course? (Indicate if this course is part of a required sequence in the major.) What other programs would this course service? Increased education access, education attainment and economic development are key priorities for the Florida region served by USF. The U. S. Labor Department, Florida Works, and regional development councils have identified key occupational growth areas anticipated in education; management and administration; nursing and health sciences; criminal justice; industrial, manufacturing, warehousing and distribution engineering; information technology and industrial applications of technology.
  27. Has this course been offered as Selected Topics/Experimental Topics course? If yes, how many times? No
  28. What qualifications for training and/or experience are necessary to teach this course? (List minimum qualifications for the instructor.) PhD
  29. Objectives: Upon completion of this course, students will:

    • Gain a perspective relative to the identification and strategic use of intelligent agents in information technology applications.

    • Become knowledgeable about specific software used to create intelligent agents.

    • Become aware of the many applications that intelligent agents can be applied to.

    • Gain a perspective relative to the potential future expanded use of intelligent agents.

  30. Learning Outcomes: • Be able to understand basic components of intelligent agent systems.

    • Be able to program intelligent agent systems.

    • Be able to solve basic application tasks using intelligent agent systems.

  31. Major Topics: Week Topic

    Week 1 Rusell & Norvig, Chapter 1 - Introduction

    Rusell & Norvig, Chapter 2 - Intelligent Agents

    Week 2 Rusell & Norvig, Chapter 3 - Solving Problems by Searching

    Rusell & Norvig, Chapter 4 - Informed Search and Exploration

    Week 3 Rusell & Norvig, Chapter 5 - Constraint Satisfaction Problems

    Rusell & Norvig, Chapter 6 - Adversarial Search

    Week 4 Test #1

    Week 5 Rusell & Norvig, Chapter 7 - Logical Agents

    Rusell & Norvig, Chapter 8 - First-Order Logic

    Week 6 Rusell & Norvig, Chapter 9 - Inference in First-Order Logic

    Rusell & Norvig, Chapter 10 - Knowledge Representation

    Week 7 Rusell & Norvig, Chapter 11 - Planning

    Rusell & Norvig, Chapter 12 - Planning and Acting in the Real World

    Week 8 Test # 2

    Week 9 Rusell & Norvig, Chapter 13 - Uncertainty

    Rusell & Norvig, Chapter 14 - Probabilistic Reasoning

    Rusell & Norvig, Chapter 15 - Probabilistic Reasoning Over Time

    Week 10 Rusell & Norvig, Chapter 16 - Making Simple Decisions

    Rusell & Norvig, Chapter 17 - Making Complex Decisions

    Week 11 Rusell & Norvig, Chapter 18 - Learning from Observations

    Rusell & Norvig, Chapter 19 - Knowledge in Learning

    Week 12 Rusell & Norvig, Chapter 20 - Statistical Learning Methods

    Rusell & Norvig, Chapter 21 - Reinforcement Learning

    Week 13 Rusell & Norvig, Chapter 22 - Communication

    Rusell & Norvig, Chapter 23 - Probabilistic Language Processing

    Week 14 Test #3

  32. Textbooks:
  33. Course Readings, Online Resources, and Other Purchases: Proposed Online Resources

    The course requires students to use the internet for investigative purposes

    Supplementary Readings

    An updated and recent reading list will be provided at the start of the semester. Other materials such as web resources, handouts, etc. will be provided in class and via Blackboard.

  34. Student Expectations/Requirements and Grading Policy: Students are expected to attend all classes on a regular basis, participate in class discussions, and turn-in assignments on-time. The grading policy will be consistent with USF policies as stated in the USF Student Handbook. Student performance will be evaluated based on 3 tests, a case study, and a term project. The relative weights for each of these components in determining the final grade are as follows:  Test #1 25% of final grade Test #2 25% of final grade Test #3 30% of final grade (cumulative) Term Project 20% of final grade

    A grade will be determined based on the total of possible points earned, as follows: A: 90 – 100, B: 80 – 89, C: 70 – 79, D: 60 – 69, F: 0 – 59

  35. Assignments, Exams and Tests: Assignments, exams and tests
  36. Attendance Policy: Course Attendance at First Class Meeting – Policy for Graduate Students: For structured courses, 6000 and above, the College/Campus Dean will set the first-day class attendance requirement. Check with the College for specific information. This policy is not applicable to courses in the following categories: Educational Outreach, Open University (TV), FEEDS Program, Community Experiential Learning (CEL), Cooperative Education Training, and courses that do not have regularly scheduled meeting days/times (such as, directed reading/research or study, individual research, thesis, dissertation, internship, practica, etc.). Students are responsible for dropping undesired courses in these categories by the 5th day of classes to avoid fee liability and academic penalty. (See USF Regulation – Registration - 4.0101,

    Attendance Policy for the Observance of Religious Days by Students: In accordance with Sections 1006.53 and 1001.74(10)(g) Florida Statutes and Board of Governors Regulation 6C-6.0115, the University of South Florida (University/USF) has established the following policy regarding religious observances: (

    In the event of an emergency, it may be necessary for USF to suspend normal operations. During this time, USF may opt to continue delivery of instruction through methods that include but are not limited to: Blackboard, Elluminate, Skype, and email messaging and/or an alternate schedule. It’s the responsibility of the student to monitor Blackboard site for each class for course specific communication, and the main USF, College, and department websites, emails, and MoBull messages for important general information.

  37. Policy on Make-up Work: No credit will be given for missed/late work unless the student has a documented medical or family emergency.
  38. Program This Course Supports: Master of Science in Information Technology
  39. Course Concurrence Information: None

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