CSCI 446/546 |

Description: | An introduction to the basic concepts of Artificial Intelligence. Topics to be covered include the history of AI, the problems treated in AI, solution techniques, state spaces, search algorithms and heuristics, expert systems, natural language processing, and robotics. Students may not take this course for both 400 and 500 level credit. Prerequisite: CSCI 332 |

Instructor: |
Michele Van Dyne mvandyne@mtech.edu (406) 496-4855 Museum 204B (2nd floor, to the left of and behind Natasha's office) Office hours: MF 10:00-11:00 MUS 204B; W 11:00-12:00; or by appointment. |

Classes: | Mon., Wed., Fri. | 9:00 - 10:00am | MAIN 109 |

Resources: | Optional Textbook | Artificial Intelligence: A Modern Approach (3rd edition) by Russell and Norvig |

Class web page | http://katie.mtech.edu/classes/csci446/ |

Undergraduate | Exams (3) | 30% |

Evaluation: |
Programs | 30% |

Homework Assignments | 20% | |

Paper | 20% | |

Staff discretion (participation, extra-credit, contests) | ±?% |

Graduate | Exams (3) | 30% |

Evaluation: |
Programs | 30% |

Homework Assignments | 10% | |

Paper and Project | 30% | |

Staff discretion (participation, extra-credit, contests) | ±?% |

Letter Grades: | |||

A | 90.00-92.99 | 93.00-100.00 | |

B | 80.00-82.99 | 83.00-86.99 | 87.00-89.99 |

C | 70.00-72.99 | 73.00-76.99 | 77.00-79.99 |

D | 60.00-62.99 | 63.00-66.99 | 67.00-69.99 |

F | 0.00-59.99 |

**Covid-19:**

We know from existing data that wearing an appropriate face covering in public can help prevent the spread of COVID-19 in the community (Lyu & Wehby, 2020; CDC, 2020; Johns Hopkins Medicine, 2020).
In accordance with policy from the Montana University System, Montana Technological University has requested that everyone wear a face covering in university buildings, including classrooms.
Face coverings have been provided for students, instructors, and staff.
If you need a face covering, ask me - I will have extras with me.
To comply with BSB Health Department regulations for contact tracing, we are required to have and use a seating chart.
If you are identified as having been in contact with someone who has tested positive in class, and you have been wearing a mask, I am told you will not be asked to quarantine.
If you were not wearing a mask...

**General:**

Any student who may need an accommodation due to a disability, please make an appointment to see me during my office hours.
A letter from a Montana Tech Disability Coordinator authorizing your accommodations is needed.

Exams must be taken at the scheduled date and time. I do not give make up exams. If you are unable to take the exam at the scheduled time, for a very valid reason, contact me in ** advance** and we will work it out.

See the assignments page for the late policy regarding assignments. NOTE: Assignment due dates are listed on the assignments page. Submission deadlines on Moodle are close to the due date and time, but because of Moodle limitations, are not exact.

I prefer that the class is interactive, so if you have questions or comments, please interrupt. If you have a question, chances are very good that others have the same question. And if I don't explain a concept clearly, stop me and I'll try again.

**Collaboration:**

Programming is a creative process and no two programmers will solve the same problem in the same way.
You are encouraged to discuss how to design a solution to a given problem with your classmates.
But when it comes time to convert your design into code, you must write the code yourself.
Be sure not to leave copies of your code where others might be able to access it (such as in the recycling bin of a lab computer).
You may adapt code from the CSCI 446 course materials and the website, provided you cite what code you used in your program's comments.

*Under no circumstances should you copy another person's code.* Copying code from another student can result in an F in the course.
Students often mistakenly believe simple transformations can disguise a copied program.
In actuality, copied programs often reveal themselves quite easily during grading.
We can also use sophisticated software such as MOSS to detect plagiarized code.

**Expectations:**

- E1. Students should have a thorough understanding of space and time complexity of data structures and algorithms. (CSCI 332)
- E2. Students should have a thorough understanding of recursion and recursive problem solving techniques, and list structures and the algorithms associated with them. (CSCI 232)
- E3. Students should have a thorough understanding of graphs, trees, and the algorithms associated with them. (CSCI 332)
- E4. Students should have a working knowledge of logic and logical methods, including propositional and predicate calculus. (CSCI 246)

- R1. Students know the historical background of the field of Artificial Intelligence.
- R2. Students are aware of the relevant ethical considerations in the field of Artificial Intelligence. (CS: 4)
- R3. Students understand and define the concept of a state space for a problem. (CS: 6)
- R4. Students can describe and implement brute-force search techniques, such as breadth-first, depth-first, and iterative deepening. (CS: 1, 2, 6)
- R5. Students can describe and implement heuristic search techniques such as greedy and A*. (CS: 1, 2, 6)
- R6. Students can describe and implement adversarial search techniques such as minimax and alpha-beta pruning. (CS: 1, 2, 6)
- R7. Students can describe and implement constraint satisfaction techniques such as backtracking and local search methods. (CS: 1, 2, 6)
- R8. Students understand the use of logic (propositional and predicate calculus) as means of representing knowledge in a computer system. (CS: 1, 2, 6)
- R9. Students can perform theorem proving using resolution in a logical knowledge-based system. (CS: 1, 2, 6)
- R10. Students understand the concept of probabilistic reasoning and can determine when to use this concept. (CS: 1, 2, 6)
- R11. Students are able to apply Bayes theorem to determine conditional probabilities and can use Bayesian networks to model and reason about problems. (CS: 1, 2, 6)
- R12. Students understand and can work with Markov models and reinforcement learning. (CS: 1, 2, 6)

Page last updated: August 18, 2021