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Syllabus

CSCI 431 - 01, Fall 2017 — 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.

About me

  • Joshua Eckroth, Assistant Professor of Computer Science, jeckroth@stetson.edu, homepage

  • Eliz Hall 214, 386-740-2519

  • Office hours: Mon 1-3, Tue 5:30-6, Wed 2-3, Thur 5:30-6

Textbook

There is no textbook for this class.

Grading

  • Attendance on work days (usually Fridays): 5%
  • Homeworks: 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.

The grading scale will follow the typical A = 93-100, A- = 90-92, B+ = 87-89, etc.

Test dates

  • Midterm: Wed Oct 11
  • Final exam: Fri Dec 15, 3:30-4:30p

Attendance

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

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.

Calendar

  • W0: Aug 25
    • Simple Prolog
  • W1: Aug 28, Aug 30, Sep 1
    • Constraint logic programming
    • Prolog recursion
  • W2: Sep 6, Sep 8
    • Prolog lists
    • Hurricane
  • W3: Sep 11, Sep 13, Sep 15
    • Hurricane
  • W4: Sep 18, Sep 20, Sep 22
    • Prolog parsing
  • W5: Sep 25, Sep 27, Sep 29
    • Prolog parsing
  • W6: Oct 4, Oct 6
    • Probabilities, Bayesian inference, ProbLog
  • W7: Oct 9, Oct 11, Oct 13
    • Review
    • Midterm Oct 11
    • Machine learning
  • W8: Oct 18, Oct 20 (No class Oct 16)
    • K-nearest neighbor classification
    • Feature engineering, text classification
  • W9: Oct 23, Oct 25, Oct 27
    • Decision trees, ensemble methods, random forests
  • W10: Oct 30, Nov 1, Nov 3
    • Neural networks, multilayer perceptrons (MLPs)
  • W11: Nov 6, Nov 8, Nov 10
    • Keras for MLPs
  • W12: Nov 13, Nov 15, Nov 17
    • Deep learning, convolutional neural networks
  • W13: Nov 20
    • Recommendation engines
  • W14: Nov 27, Nov 29, Dec 1
    • Recommendation engines
  • W15: Dec 4, Dec 6, Dec 8
    • Reinforcement learning
  • W16: Dec 11, Dec 13
    • Reinforcement learning
  • Final exam: Fri Dec 15, 3:30-4:30

Honor code

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 asc@stetson.edu.

CSCI 431 material by Joshua Eckroth is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License. Source code for this website available at GitHub.