Link

Home Syllabus

Syllabus

Course Info

Professor: Dr Eren Gultepe
Class Times: TR, 11:00 am - 12:15 pm
Lecture Location: Science East 0214

Office Hours: After lecture or by appointment
Office: EB 3071
Email: egultep@siue.edu
Phone: (618) 650-2389

Course Description

This course introduces methods and techniques related to the implementation of autonomous computer systems. We will survey different topics related to the development of artificial intelligence, starting from the concept of intelligence and concluding with philosophical implications.

Course Organization

All content will be made available online, which includes homework, projects, and lecture videos. Course follows a “flipped” format. It is highly recommended for you to watch lecture videos before class meeting times. During lecture times which will be conducted as Discussion/Tutorial section, we will review worksheets and solve problems related to material that will be covered on exams and written homework assignments. And if there are any questions related to the programming assignments, they will be answered at that time.

Course Prerequisites

Assessment Criteria

The evaluations comprising a student’s grade in the course are:

Grading Scale

The grading scale (pre-curved or curve included) for the course is:

A ≥ 80%; B ≥ 65%; C ≥ 55%; D ≥ 50%

Topics Covered

  1. Search
    • Informed and Uniformed search
    • Constraint Satisfaction Problems
    • Game Trees
  2. Probability
    • Markov Decision Processes
    • Reinforcement Learning
    • Bayesian Networks
    • Hidden Markov Models

Written Assignments

There will be 4 written assignments that review concepts covered in discussion and worksheet review.

Programming Projects

There will be 4 programming projects. Project 0 is to be completed alone and is only for practice setting up your Python environment. Projects 1 through 4 can be completed alone or with a partner, but must still be submitted individually. If completed with a partner, then both members need to indicate who their partner was on their submissions. The code must be properly commented, clearly explaining what the code is doing, especially on key components.

Late or missed assignments/exams

No makeup exams or assignments will be given. If you miss an exam or assignment, there must be documentation in writing, provided to me or to the department administrative assistant. I will still review whether you have exercised due diligence. Otherwise, you will receive 0 points for the missed exam or assignment.

Textbooks

The necessary reading material will be provided on the course website. A few written homework problems will be assigned from the 3rd or 4th edition of Artificial Intelligence: A Modern Approach, by Stuart Russell andPeter Norvig.

Academic Honesty

Plagiarism is the use of another person’s words or ideas without crediting that person. Plagiarism and cheating will not be tolerated and may lead to failure on an assignment, in the class, or dismissal from the University, per the SIUE academic dishonesty policy. Students are responsible for complying with University policies about academic honesty as stated in the University’s Student Academic Conduct Code.

Academic Integrity

Students are reminded that the expectations and academic standards outlined in the Student Academic Code (3C2) apply to all courses, field experiences and educational experiences at the University, regardless of modality or location. The full text of the policy can be found here: Student Academic Code.

Recordings of Class Content

Faculty recordings of lectures and/or other course materials are meant to facilitate student learning and to help facilitate a student catching up who has missed class due to illness. As such, students are reminded that the recording, as well as replicating or sharing of any course content and/or course materials without the express permission of the instructor of record, is not permitted, and may be considered a violation of the University’s Student Conduct Code (3C1), linked here: Student Conduct Code.

Accessibility

Students needing accommodations because of medical diagnosis or major life impairment will need to register with Accessible Campus Community & Equitable Student Support (ACCESS) and complete an intake process before accommodations will be given. The ACCESS office is located in the Student Success Center, Room 1270. You can also reach the office by e-mail at myaccess@siue.edu or by calling (618) 650-3726. For more information on policies, procedures, or necessary forms, please visit the ACCESS website at www.siue.edu/access.

COVID-19 Policies

Absences

Throughout the semester, all lectures slides and videos, course assignments, and due dates will be posted on the course website. Also, all relevant course communications will be made through email and Blackboard announcements. Thus, no Zoom links of the lectures will be provided since all relevant course material will be available online.

Since you are encouraged to work in partners for your written and coding homework, for any short unplanned absences, your partners will be a vaulable resource (e.g., for obtaining any class notes). This will ensure a students’ continued progress in the course and if needed I will to help balance the circumstances regarding the absence.

In case of accommodations requested by the ACCESS office for medical diagonsis or major life impairment, the student’s absence will be accomadated on a case-by-case basis. Please see Accessibility or Accommodations)

At the discretion of the instructor, all material, assignments, and deadlines are subject to change with prior notice. It is your responsibility to stay in touch with your instructor, review the course site regularly, or communicate with other students, to adjust as needed if assignments or due dates change.