695.737.81 - AI for Assured Autonomy

Expanded Course Description

This course covers basic concepts, principles, and fundamental approaches needed to create artificial intelligent agents. The main topics include AI Fundamentals, Probabilistic Inference, Advanced Python Programming. Supervised Machine Learning, Unsupervised Machine Learning, Neural Networks, Reinforcement Learning, Deep Learning, Natural Language Processing, Decision Tree/Search Algorithms and Assured Autonomous Systems.  There will be assignments, quizzes, exams and Projects that will reinforce the learning experience and provide a strong understanding of the importance of artificial intelligence in assured autonomy. 

Course Structure

The course materials are divided into modules which can be accessed by clicking Course Modules on the left menu. A module will have several sections including the overview, content, readings, discussions, and assignments. You are encouraged to preview all sections of the module before starting. Most modules run for a period of seven (7) days, exceptions are noted in the Course Outline. You should regularly check the Calendar and Announcements for assignment due dates.

Course Topics

Course Goals

To introduce and expose students to the fundamental approaches needed to create assured autonomous artificial intelligent agents. Students will develop an understanding of the fundamental concepts, theory, applications and modeling Techniques needed  for assuring AI enabled autonomous systems. Students will implement the latest state-of-the-art algorithms and be exposed to the industry leading tools needed to create agents and models to solve various problems with the use Artificial Intelligent Agents.

Course Learning Outcomes (CLOs)

• By the end of this course, you will be able to: • Explain theoretical principles and practical approaches to Artificial Intelligence. • Evaluate machine learning models using probability and statistics. • Utilize open sourced programming languages such as Python/R Programming to perform analyses and transform datasets • Create and evaluate highly predictive supervised machine learning models in Python • Create and evaluate unsupervised machine learning models (clustering and Feature Reduction). • Create Hidden Markov Models • Create Neural Network • Create Deep Learning models using TensorFlow/Keras and advanced neural networks. • Perform natural language processing on various datasets using Python. • Evaluate datasets using Explainable Artificial Intelligence. • Perform Robotic Autonomous Search

Textbooks

Textbook

Required

Pattern Recognition and Machine Learning, Christopher M. Bishop, 2006, Springer Science + Business Media, LLC ISBN-10: 0-387-31073-8   ISBN-13: 978-0387-31073-2

Python Data Science Handbook, Jake Vander Plas 2006, O’Reilly Media, Inc., ISBN-13:  978-1491912058 ISBN-10:  1491912057

Natural Language Processing with Python, Steven Bird, Ewan Klein, and Edward Loper 2009

Textbook information for this course is available online through the appropriate bookstore website: For online courses, search the MBS website.MBS website

Required Software

Python

You will need access to a recent version of Python 3.6. Python being an open source tool is free to download and will not require a specific license from JHU.

Student Coursework Requirements

It is expected that each module will take approximately 7–10 hours per week to complete. Here is an approximate breakdown: reading the assigned sections of the texts (approximately 3–4 hours per week) as well as some outside reading, listening to the audio annotated slide presentations (approximately 2–3 hours per week), and writing assignments (approximately 2–3 hours per week).

This course will consist of the following basic student requirements:

Weekly Discussion Participation and Mini Quizzes(12% of Final Grade Calculation)

You are responsible for carefully reading all assigned material and being prepared for discussion. The majority of readings are from the course text. Additional reading may be assigned to supplement text readings.

Post your initial response to the discussion questions/mini quizzes by the evening of day 3 for that module week. Posting a response to the discussion question is part one of your grade for module discussions (i.e., Timeliness).

Part two of your grade for module discussion is your interaction (i.e., responding to classmate postings with thoughtful responses) with at least two classmates (i.e., Critical Thinking). Just posting your response to a discussion question is not sufficient; we want you to interact with your classmates. Be detailed in your postings and in your responses to your classmates' postings. Feel free to agree or disagree with your classmates. Please ensure that your postings are civil and constructive.

I/We will monitor module discussions and will respond to some of the discussions as discussions are posted. In some instances, I/we will summarize the overall discussions and post the summary for the module.

Evaluation of preparation and participation is based on contribution to discussions.

Weekly Discussion are evaluated by the following grading elements:

1. Timeliness (50%)
2. Critical Thinking (50%)

Mini Quizzes are evaluated by the following grading elements:

1. Timeliness (10%)
2. Accuracy (90%)

Preparation and participation is graded as follows:

• 100–90 = A—Timeliness [regularly participates; all required postings; early in discussion; throughout the discussion]; Critical Thinking [rich in content; full of thoughts, insight, and analysis].
• 89–80 = B—Timeliness [frequently participates; all required postings; some not in time for others to read and respond]; Critical Thinking [substantial information; thought, insight, and analysis has taken place].
• 79–70 = C—Timeliness [infrequently participates; all required postings; most at the last minute without allowing for response time]; Critical Thinking [generally competent; information is thin and commonplace].
• <70 = F—Timeliness [rarely participates; some, or all required postings missing]; Critical Thinking [rudimentary and superficial; no analysis or insight is displayed].

Homework Assignments (10% of Final Grade Calculation)

There will be 5 graded homework assignments that students will complete individually.  Each assignment will be worth 2% of the overall grade. (10% of total grade Assignments will include a mix of qualitative assignments (e.g. model summaries/analyses), quantitative problem sets, and case study updates. Include a cover sheet with your name and assignment identifier. Also include your name and a page number indicator (i.e., page x of y) on each page of your submissions., and conclusions/discussion delineated. All Figures and Tables should be captioned and labeled appropriately.

All assignments are due according to the dates in the Calendar.

Late submissions will be reduced by one letter grade for each week late (no exceptions without prior coordination with the instructors).

If, after submitting a written assignment you are not satisfied with the grade received, you are encouraged to redo the assignment and resubmit it. If the resubmission results in a better grade, that grade will be substituted for the previous grade.

Qualitative assignments are evaluated by the following grading elements:

1. Each part of question is answered (20%)
2. Writing quality and technical accuracy (30%) (Writing is expected to meet or exceed accepted graduate-level English and scholarship standards. That is, all assignments will be graded on grammar and style as well as content.)
3. Rationale for answer is provided (20%)
4. Examples are included to illustrate rationale (15%) (If you do not have direct experience related to a particular question, then you are to provide analogies versus examples.)

Qualitative assignments are graded as follows:

• 100–90 = A—All parts of question are addressed; Writing Quality/ Rationale/ Examples/ Outside References [rich in content; full of thought, insight, and analysis].
• 89–80 = B—All parts of the question are addressed; Writing Quality/ Rationale/ Examples/ Outside References [substantial information; thought, insight, and analysis has taken place].
• 79–70=C—Majority of parts of the question are addressed; Writing Quality/ Rationale/ Examples/ Outside References [generally competent; information is thin and commonplace].
• <70=F—Some parts of the question are addressed; Writing Quality/ Rationale/ Examples/ Outside References [rudimentary and superficial; no analysis or insight displayed].

Quantitative assignments are evaluated by the following grading elements:

1. Each part of question is answered (20%)
2. Assumptions are clearly stated (20%)
3. Intermediate derivations and calculations are provided when needed (25%)
4. Answer is technically correct and is clearly indicated (25%)
5. Answer precision and units are appropriate (10%)

Quantitative assignments are graded as follows:

• 100–90 = A—All parts of question are addressed; All assumptions are clearly stated; All intermediate derivations and calculations are provided; Answer is technically correct and is clearly indicated; Answer precision and units are appropriate.
• 89–80 = B—All parts of question are addressed; All assumptions are clearly stated; Some intermediate derivations and calculations are provided; Answer is technically correct and is indicated; Answer precision and units are appropriate.
• 79–70=C—Most parts of question are addressed; Assumptions are partially stated; Few intermediate derivations and calculations are provided; Answer is not technically correct but is indicated; Answer precision and units are indicated but inappropriate.
• <70=F—Some parts of the question are addressed; Assumptions are not stated; Intermediate derivations and calculations are not provided; The answer is incorrect or missing; The answer precision and units are inappropriate or missing.

Course Project (30% of Final Grade Calculation)

There will be two(2) individual course projects will be assigned several weeks into the course. The first project will be assigned at the midpoint of the course (Module 6) and one at the end of the course (Module 12). In each of these projects, students will be required to work individually to effectively apply the tools and techniques presented in each half of the course to two real-world scenarios involving Supervised and Unsupervised Machine Learning Modeling (Module 7) and Explainable Artificial Intelligence (Module 12).   

Students will be required to critique one of their classmates’ presentations and their approach to using these advanced techniques to solve the Problems. Each student will be required to submit a written report outlining their approaches to solving the problems, detailed strategy, evaluation KPIS and finally code that was used to solve the problem. I will grade the presentations, written reports and code using rubrics, which will be distributed when each project is assigned. (30% of total grade / 15% per project)

The course project is evaluated by the following grading elements:

1. Student technical understanding of the course project topic (as related to individual role that the student assumes and described in the Course Project Description) (20%)
2. Team preparation and participation (as described in Course Project Description) (20%)
3. Team technical understanding of the course project topic (as related to the Customer Team roles assumed by the students and the Seller Team roles assumed by the students and described in the Course Project Description) (20%)

Course Project is graded as follows:

• 100–90 = A—Student Preparation and Participation/ Team Preparation and Participation [individual roles and responsibilities well defined and understood; individual/ team well versed in use of Adobe Connect; individual/ work product(s) agreed to, well prepared and available to all team members/ instructors]; Student Understanding/ Team Understanding [rich in content; full of thought, insight, and analysis].
• 89–80 = B—Student Preparation and Participation/ Team Preparation and Participation [individual/ team roles and responsibilities well defined and understood; individual/ team well versed in use of Adobe Connect; individual/ team work product(s) agreed to and prepared]; Student Understanding/ Team Understanding [substantial information; thought, insight, and analysis has taken place].
• 79–70 = C—Student Preparation and Participation/ Team Preparation and Participation [individual/ team roles and responsibilities agreed to; individual/ team well versed in use of Adobe Connect; individual/ team work product(s) prepared]; Student Understanding/ Team Understanding [generally competent; information is thin and commonplace].
• <70 = F—Student Preparation and Participation/individual roles and responsibilities not well understood; individual has difficult with use of Adobe Connect; individual/ team work product(s) partially prepared]; Student Understanding/ Team Understanding [rudimentary and superficial; no analysis or insight displayed].

Quizzes (18% of Final Grade Calculation

The quizzes will assess students’ achievement of the learning objectives in modules 1-2, 3-4, 5-6, 7-9,10-11 and 12-13 respectively. (18 % of total grade)

The quizzes are evaluated by the following grading elements:

1. Each part of question is answered correctly (100%)

Quizzes are graded as follows:

• 100–90 = A—All parts of question are addressed; Writing Quality/ Rationale/ Examples/ Outside References [rich in content; full of thought, insight, and analysis].
• 89–80 = B—All parts of the question are addressed; Writing Quality/ Rationale/ Examples/ Outside References [substantial information; thought, insight, and analysis has taken place].
• 79–70 = C—Majority of parts of the question are addressed; Writing Quality/ Rationale/ Examples/ Outside References [generally competent; information is thin and commonplace].
• <70 = F—Some parts of the question are addressed; Writing Quality/ Rationale/ Examples/ Outside References [rudimentary and superficial; no analysis or insight displayed].

Exams (30% of Final Grade Calculation, combined from 15% for Exam1 and 15% for Exam2)

The midterm exam will be available in Module 6 and the final exam will be available in the next-to-last Module. You will have one week to complete the exams and they will be due by 5PM exactly one week from their release. You may use the course text to complete the exams.

The exams are evaluated by the following grading elements:

2. Each part of question is answered (20%)
3. Writing quality and technical accuracy (30%) (Writing is expected to meet or exceed accepted graduate-level English and scholarship standards. That is, all assignments will be graded on grammar and style as well as content.)
4. Rationale for answer is provided (20%)
5. Examples are included to illustrate rationale (15%)

Exams are graded as follows:

• 100–90 = A—All parts of question are addressed; Writing Quality/ Rationale/ Examples/ Outside References [rich in content; full of thought, insight, and analysis].
• 89–80 = B—All parts of the question are addressed; Writing Quality/ Rationale/ Examples/ Outside References [substantial information; thought, insight, and analysis has taken place].
• 79–70 = C—Majority of parts of the question are addressed; Writing Quality/ Rationale/ Examples/ Outside References [generally competent; information is thin and commonplace].
• <70 = F—Some parts of the question are addressed; Writing Quality/ Rationale/ Examples/ Outside References [rudimentary and superficial; no analysis or insight displayed].

Grading Policy

Grading

Assignments are due according to the dates posted in your Canvas course site. You may check these due dates in the Course Calendar or the Assignments in the corresponding modules. I/We will post grades one week after assignment due dates.

We generally do not directly grade spelling and grammar. However, egregious violations of the rules of the English language will be noted without comment. Consistently poor performance in either spelling or grammar is taken as an indication of poor written communication ability that may detract from your grade.

A grade of A indicates achievement of consistent excellence and distinction throughout the course—that is, conspicuous excellence in all aspects of assignments and discussion in every week.

A grade of B indicates work that meets all course requirements on a level appropriate for graduate academic work. These criteria apply to both undergraduates and graduate students taking the course.

EP uses a +/- grading system (see “Grading System”, Graduate Programs catalog, p. 10).

100-98 = A+ 97-94 = A 93-90 = A− 89-87 = B+ 86-83 = B 82-80 = B− 79-77 = C+ 76-73 = C 72-70 = C− 69-67 = D+ 66-63 = D <63 = F

Final grades will be determined by the following weighting:

Academic Policies

Deadlines for Adding, Dropping and Withdrawing from Courses

Students may add a course up to one week after the start of the term for that particular course. Students may drop courses according to the drop deadlines outlined in the EP academic calendar (https://ep.jhu.edu/student-services/academic-calendar/). Between the 6th week of the class and prior to the final withdrawal deadline, a student may withdraw from a course with a W on their academic record. A record of the course will remain on the academic record with a W appearing in the grade column to indicate that the student registered and withdrew from the course.

Academic Misconduct Policy

All students are required to read, know, and comply with the Johns Hopkins University Krieger School of Arts and Sciences (KSAS) / Whiting School of Engineering (WSE) Procedures for Handling Allegations of Misconduct by Full-Time and Part-Time Graduate Students.

This policy prohibits academic misconduct, including but not limited to the following: cheating or facilitating cheating; plagiarism; reuse of assignments; unauthorized collaboration; alteration of graded assignments; and unfair competition. Course materials (old assignments, texts, or examinations, etc.) should not be shared unless authorized by the course instructor. Any questions related to this policy should be directed to EP’s academic integrity officer at ep-academic-integrity@jhu.edu.

Students with Disabilities - Accommodations and Accessibility

Johns Hopkins University values diversity and inclusion. We are committed to providing welcoming, equitable, and accessible educational experiences for all students. Students with disabilities (including those with psychological conditions, medical conditions and temporary disabilities) can request accommodations for this course by providing an Accommodation Letter issued by Student Disability Services (SDS). Please request accommodations for this course as early as possible to provide time for effective communication and arrangements.

For further information or to start the process of requesting accommodations, please contact Student Disability Services at Engineering for Professionals, ep-disability-svcs@jhu.edu.

Student Conduct Code

The fundamental purpose of the JHU regulation of student conduct is to promote and to protect the health, safety, welfare, property, and rights of all members of the University community as well as to promote the orderly operation of the University and to safeguard its property and facilities. As members of the University community, students accept certain responsibilities which support the educational mission and create an environment in which all students are afforded the same opportunity to succeed academically. 

For a full description of the code please visit the following website: https://studentaffairs.jhu.edu/policies-guidelines/student-code/

Classroom Climate

JHU is committed to creating a classroom environment that values the diversity of experiences and perspectives that all students bring. Everyone has the right to be treated with dignity and respect. Fostering an inclusive climate is important. Research and experience show that students who interact with peers who are different from themselves learn new things and experience tangible educational outcomes. At no time in this learning process should someone be singled out or treated unequally on the basis of any seen or unseen part of their identity. 
 
If you have concerns in this course about harassment, discrimination, or any unequal treatment, or if you seek accommodations or resources, please reach out to the course instructor directly. Reporting will never impact your course grade. You may also share concerns with your program chair, the Assistant Dean for Diversity and Inclusion, or the Office of Institutional Equity. In handling reports, people will protect your privacy as much as possible, but faculty and staff are required to officially report information for some cases (e.g. sexual harassment).

Course Auditing

When a student enrolls in an EP course with “audit” status, the student must reach an understanding with the instructor as to what is required to earn the “audit.” If the student does not meet those expectations, the instructor must notify the EP Registration Team [EP-Registration@exchange.johnshopkins.edu] in order for the student to be retroactively dropped or withdrawn from the course (depending on when the "audit" was requested and in accordance with EP registration deadlines). All lecture content will remain accessible to auditing students, but access to all other course material is left to the discretion of the instructor.