ME 467 Machine Intelligence
An introduction to artificial/machine intelligence. The study of this evolving and diverse topic begins with a survey and classification of techniques, including search-based, logic-based, statistical, and embodied. Applications of intelligent technologies explored include natural language processing, vision, expert knowledge, game-playing, and several robotics applications. Upon conclusion of the survey, the focus of the course turns to a special topic chosen by the instructor. The instructor could choose, for instance, machine learning, embodiment, evolutionary robotics, or artificial life—or a project.
An introduction to artificial/machine intelligence. The study of this evolving and diverse topic begins with a survey and classification of techniques, including search-based, logic-based, statistical, and embodied. Applications of intelligent technologies explored include natural language processing, vision, expert knowledge, game-playing, and several robotics applications. Upon conclusion of the survey, the focus of the course turns to a special topic chosen by the instructor. The instructor could choose, for instance, machine learning, embodiment, evolutionary robotics, or artificial life—or a project.
Course description
An introduction to artificial/machine intelligence. The study of this evolving and diverse topic begins with a survey and classification of techniques, including search-based, logic-based, statistical, and embodied. Applications of intelligent technologies explored include natural language processing, vision, expert knowledge, game-playing, and several robotics applications. Upon conclusion of the survey, the focus of the course turns to a special topic chosen by the instructor. The instructor could choose, for instance, machine learning, embodiment, evolutionary robotics, or artificial life—or a project.
General information
Instructor Rico AR Picone, PhD Actual office hours (CH 103C) M,W 2:50–3:50 T,Th 2:20–3:50 Virtual office hours (zoom, make appointment!) M,W 2:50–3:50 T,Th 2:20–3:50 Virtual office hours appointments make appointment Office location CH 103C Classroom location Panowicz Hall 107 Times MW 2:00–4:20 pm Moodle moodle.stmartin.edu
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Textbooks
Stuart J. Russell and Peter Norvig. Artificial Intelligence: A Modern Approach. Fourth Edition. Pearson Education, 2020. (Required. Henceforth: RN)
Artificial Intelligence: A Modern Approach.
Ian Goodfellow, Yoshua Bengio, Aaron Courville. Deep Learning. MIT Press, 2016. (Recommended to extend your understanding of deep learning.)
Rolf Pfeifer and Josh Bongard. How the Body Shapes the Way We Think: A New View of Intelligence. MIT Press, 2007. (Recommended to extend your understanding of embodied cognition.)
How the Body Shapes the Way We Think: A New View of Intelligence.
Schedule
The following schedule is tentative. All assignments will be set one week before the due date.
| week | topics introduced | reading | project | due |
|---|---|---|---|---|
| course introduction, setting up your development environment | RN 1 Introduction 2 Intelligent Agents |
set up your dev environment, organize into teams | ||
| TBA | RN 3 Solving Problems by Searching 4 Search in Complex Environments |
set up collab environment (GitHub) and report doc | assignment TBA | |
| TBA | RN 6 Constraint Satisfaction Problems 19 Learning from Examples |
write control program with given controller | assignment TBA | |
| TBA | RN 21 Deep Learning 22 Reinforcement Learning |
begin writing ML controller | assignment TBA | |
| TBA | TBA | continue writing ML controller | assignment TBA | |
| TBA | 26 Robotics | write report | project due |
| Day | Date | Topics | Reading | Due |
|---|
Assignments
TBA
Assignments
No assignments found.
Development environment
The following development environment will be used for all class examples and assignments. The three primary pieces of software, Anaconda (Python), Git, and VS Code are available for free on all major operating systems. All of this except VS Code is installed on engineering lab computers (including those in the classroom).
Anaconda
Anaconda is a distribution of Python with a collection of Python packages for data science.
Download and install Anaconda.
Git
Git is an amazing version control tool for code development and collaboration. I use it for everything.
Install Git from the instructions here.
VS Code
This is currently the most popular code editor/integrated development environment (IDE) for many programming languages and tasks.
Setting up your VS Code environment
Do the following to set up your VS Code environment:
Download and install VS Code. It is available on all major platforms. It can be installed on your user account on a lab computer.
Open VS Code.
Under the “Extensions” tab (left-side navigation), install the Python extension.
Windows users: type ctrl+``` to open a terminal. Click the dropdown next to the + sign and select “Select Default Profile.” From the dropdown, select “Git Bash.”
Type ctrl+shift+P (macOS cmd+shift+p), type Python: Select Interpreter, and press Enter. Select from the list the python.exe file in an anaconda directory with the name base. This selects the Anaconda environment base. This should already contain the packages we will need.
Create a new file with ctrl+N. Write the following in the file:
import numpy as np
import sympy as sp
import matplotlib.pyplot as plt
print("Hello, world!"
Click the “play” button in the top right (Run Python File in Dedicated Terminal). The terminal should display the output Hello, world!.
Create a new file with ctrl+N. Write the following in the file:
import numpy as np
import sympy as sp
import matplotlib.pyplot as plt
print("Hello, world!")
import numpy as np
import sympy as sp
import matplotlib.pyplot as plt
print("Hello, world!"
Projects
The projects will create machine learning controllers (MLCs) to control dynamic systems. The groups will be given two multi-input, multi-output (MIMO) dynamic system plant models to control:
With the Nice Model, you will be given a Benchmark Controller. The design goals are
Performance criteria
TBA
Project organization
Projects will have the following organization.
The class will be divided into two teams.
Each team will have its own GitHub repository. Create a GitHub account and message me your username so I can add you to your repository.
Each team will collaborate to
Deep Learning : uses a deep learning method to design the controllers.
Reinforcement Learning : uses a reinforcement learning method to design the controllers.
Franny (co-lead)
Robert (co-lead)
Justus
Jonathan (co-lead)
Kyler (co-lead)
Kristopher
Reno
Reinforcement Learning Project
research their method of MLC,
design the MLC controllers,
write programs that demonstrate their controllers’ performance, and
write a report according to the report guidelines.
Report guidelines
TBA
Graduate student responsibilties
Graduate students (and students taking the course at the graduate level) have additional responsiblities in the course. They include:
Homework, quiz, & project policies
Homework & homework quiz policies
Weekly homework will be “due” on Fridays, but it will not be turned in for credit. However — and this is very important — each week a quiz will be given on Friday that will cover that week’s homework.
Quizzes will be available on moodle each Friday (as early as I can get them up), and must be completed by that evening (before midnight). Late quizzes will receive no credit.
Working in groups on homework is strongly encouraged, but quizzes must be completed individually.
Grading policies
Total grades in the course may be curved, but individual homework quizzes and exams will not be. They will be available on moodle throughout the semester.
Homework quizzes 20% Midterm Exam #1 25% Midterm Exam #2 25% Final Exam 30%
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Academic integrity policy
Cheating or plagiarism of any kind is not tolerated and will result in a failing grade (“F”) in the course. I take this very seriously. Engineering is an academic and professional discipline that requires integrity. I expect students to consider their integrity of conduct to be their highest consideration with regard to the course material.
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Page updated 06 September 2023 at 23:20
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Rico A.R. Picone, PhD
Rico A.R. Picone, PhD
Dr. Rico to you
Dr. Rico to you
Page updated 06 September 2023 at 23:20
Academic Honesty/Professionalism
What is Academic Integrity?
Saint Martin's University is a community of faculty, students and staff engaged in the exchange of ideas in the ongoing pursuit of academic excellence. Essential to our mission is a focused commitment to scholarly values and intellectual integrity, and a respect for the ideas, beliefs and work of others. This commitment extends to all aspects of academic performance. All members are expected to abide by ethical standards both in their conduct and their exercise of responsibility to themselves and toward other members of the community. As an expression of our shared belief in the Benedictine tradition, we support the intellectual, social, emotional, physical and spiritual nurturing of students.
What is Academic Dishonesty?
Saint Martin's University defines academic dishonesty as violating the academic integrity of an assignment, test and/or evaluation of any coursework. This dishonest practice occurs when you seek to gain for yourself or another an academic advantage by deception or other dishonest means. You have a responsibility to understand the requirements that apply to particular assessments and to be aware of acceptable academic practice regarding the use of material prepared by others. Therefore, it is your responsibility to be familiar with the policies surrounding academic dishonesty as these may differ from other institutions.
The Acceptable Use of AI in Coursework
Any use of technology that misleads a reviewer in assessing the student's mastery of a specific set of skills or knowledge is a type of intellectual dishonesty, that is, a type of cheating. Students who are unsure about the appropriateness of using an artificial intelligence tool (or "AI") must check with the instructor before using it. This includes the use of tools that generate text, images, video, code, and other works. If you are permitted by your instructor to use one or more AI tools in producing your work, you must disclose the use of that tool. You should say which tool you used and how you used it. Then if you use specific AI generated content (text, images, videos, audio, code, and so on) you must cite it in the style (APA, MLA, and so on) specified by your instructor.
University-Sanctioned Activities
If you are absent from class due to university-sanctioned activities, such as sports, it is your responsibility to request that the absence be excused; otherwise, the absence will be recorded as unexcused. Absent students are responsible for catching up with the class, and if any assignments are due on the day of the absence, it is your responsibility to turn in the assignments on time (prior to class). Assignments may be submitted as an attachment to the instructor's email address.
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Your experience in this class is important to me. If you have already established accommodations with Disability Support Services (DSS), please communicate your approved accommodations to me at your earliest convenience so we can discuss your needs in this course. If you have not yet established services through DSS, but have a temporary health condition or permanent disability that requires accommodations (conditions include but are not limited to mental health, attention-related, learning, vision, hearing, physical or health impacts), you are welcome to contact DSS at 360-438-4580 or smu.dss@stmartin.edu. DSS offers resources and coordinates reasonable accommodations for students with disabilities and/or temporary health conditions. Reasonable accommodations are established through an interactive process between you, your instructor(s) and DSS. It is the policy and practice of Saint Martin's University to create inclusive and accessible learning environments consistent with federal and state laws.
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