Fall 2017 Artificial Intelligence: Course Policies
- Explain the different informed and uninformed search techniques, types of heuristics and when optimality is achieved.
- Solve constrained satisfaction using guaranteed and heuristic methods.
- Implement alpha-beta pruning to solve min-max games; design good evaluation functions and optimize search time using a variety of methods to increase speed of search.
- Design and implement algorithms to learn hidden Markov models.
- Utilize reinforced learning to discover patterns in data and make informed decisions.
The course reading will be from the textbook Artificial Intelligence A Modern Approach. We will be doing the readings online using the website Perusall, so you should not purchase the text unless you want to pay for both online and print versions. The readings need to be completed before the class where the topic will be discussed. You will need a code for access to the Perusall site that you will obtain in class. To receive full credit for the readings you must participate in the discussions for each reading. An average of 1.5 points per reading will result in full credit.
There will be ungraded homework assignments each week. These will provide opprotunities to practice various types of problems in preparation for quizzes and exams. Solutions for these problems will be discussed in class.
Their will be weekly quizzes. Approximately half of each quiz will come from recent homework problems (possibly with minor modifications). The lowest 10% of scores will be dropped. Makeups for quizzes will not be given except under extreme situations.
The main products you will be producing in this course are entries into four different programming contests. You will be competing against your classmates and the intrsutors reference implementations. These will include a solver for the 15 puzzle, bots to play the games Pente and Backgammon and a bot to capture the ghosts in a Pacman variant. During the two weeks leading up to each of these contests, there will be online tests servers running with class rankings. You are expected to submit early and often. The in-class finals are tentatively scheduled for
|15 Puzzle||September 15|
There will be a midterm and final covering the written material in the class. The midterm is tentatively scheduled for Wednesday, October 18. The final will be Monday, December 18, 8:00-9:50.
The breakdown of grades is as follows.
Total scores of at least 70%, 80% and 90% will ensure you receive at least a C-, B-, or A- in the course, respectively.
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Student Success Center Syllabi Statements
In an effort to be inclusive of students' learning styles and needs with regard to academic support, the following statement has been developed for use in course syllabi which identifies resources for student support in various areas of learning. As faculty members construct their syllabi for future courses, it is requested that they update materials to include new language regarding academic and career related support offered to students through the Student Success Center.
The syllabus statement will be available throughout the year by visiting the following websites, but we encourage you to place these resources on any departmental websites you feel necessary:
- Student Success Center: www.slu.edu/success
- Reinert Center for Transformative Teaching & Learning: www.slu.edu/cttl
Student Success Center Syllabus Statement
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