COURSE SYLLABUS

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 determined that everyone will be required to wear a face covering in university buildings, including classrooms. You MUST wear a face covering appropriately (i.e., covering both your mouth and nose) in the building when you are attending class in person. Face coverings have been provided for students, instructors, and staff, and everyone is expected to wear one while inside any university building.

Anyone attending class in person without a face covering will be asked to put one on or leave. Instructors will have the option to end class if anyone present refuses to appropriately wear a face covering for the duration of class. Students who refuse to wear face coverings appropriately or adhere to other stated requirements may face disciplinary action for Code of Conduct violations. On a case-by-case basis, students may consult with the Dean of Students and/or Student Disability Resources.

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 16, 2021