CSCI 431 - 01, Fall 2016 — Artificial Intelligence
MWF 3:30-4:20p Eliz 210; pre-reqs: CSCI 221
This course covers a wide array of topics related to contemporary artificial intelligence and machine learning techniques and tools. Our focus will be on practical techniques that can be immediately applied outside of the classroom. By the end of the course, you should be able to apply an appropriate AI technique in a novel situation.
Eliz Hall 214, 386-740-2519
Office hours: Mon/Wed 12-2:30
There is no textbook for this class.
Several assignments require that you understand and write Python code, including the first assignment. You will need to learn Python on your own, though we will spend a small amount of time reviewing the language in class. In all assignments involving Python, you will start with existing code that must be modified or extended. For quickly learning the essentials, I suggest the website Learn X in Y Minutes, where X=Python.
- Attendance on work days (usually Fridays): 5%
- Homeworks (10 of them, 1 extra credit): 55%
- Midterm: 20%
- Final exam: 20%
Late work is penalized 20% for each day that it is late. Submissions more than 3 days late receive no credit. Tests cannot be made up unless a doctor’s note is provided.
- Midterm: Fri Oct 28
- Final exam: Tue Dec 13, 1-3pm
The grading rubric for attendance is as follows, out of 3 points:
- attended at least 75% of work days: 3 pts
- attended at least 50% of work days: 2 pts
- attended at least 25% of work days: 1 pt
- attended fewer than 25% of work days: 0 pts
Homework will be submitted via Bitbucket. Create an account on Bitbucket and, for every assignment, create a new repository and add me (username ‘joshuaeckroth’) as a reader. Always name the repository
csci431-A01 or similar.
See the individual assignments for the grading rubric. Homeworks are always out of 5 points.
- Week 1: Search
- Week 2: Planning
- Week 3: Adversarial search
- Week 4: Genetic algorithms
- Week 5: Expert systems
- Week 6: Prolog
- Week 7: Abductive reasoning
- Week 8: Midterm
- Week 9: Probabilities
- Week 10: Classification
- Week 11: Neural networks / deep learning
- Week 12: Text processing and classification
- Week 13: Robotics
- Week 14: Robotics
- Week 15: Final exam
Homework due dates:
- A01: Organic pathfinding, due Sep 7, 11:59pm
- A02: Git planner, due Sep 12, 11:59pm
- A03: Connect Four AI, due Sep 19, 11:59pm
- A04: Wedding seat assignment, due Sep 26, 11:59pm
- A05: Student advisor, due Oct 3, 11:59pm
- A06: Prolog Pokédex, due Oct 26, 11:59pm
- A07: Automated jury, due Nov 9, 11:59pm
- A08: NFL play-by-play, due Nov 21, 11:59pm
- A09: Recipe classification, due Nov 30, 11:59pm
- A10: Multilayer perceptrons, due Dec 7, 11:59pm
- A11: Cats vs Dogs [Extra credit], due Dec 14, 11:59pm
I am strongly in agreement with the Stetson University Honor Code. Any form of cheating is not acceptable, will not be tolerated, and could lead to dismissal from the University.
Academic success center
If a student anticipates barriers related to the format or requirements of a course, she or he should meet with the course instructor to discuss ways to ensure full participation. If disability-related accommodations are necessary, please register with the Academic Success Center (822-7127; www.stetson.edu/asc) and notify the course instructor of your eligibility for reasonable accommodations. The student, course instructor, and the Academic Success Center will plan how best to coordinate accommodations. The Academic Success Center is located at 209 E Bert Fish Drive, and can be contacted using the email address email@example.com.