DSCI 6612 – 01
Artificial Intelligence
Spring 2022
Meeting Times and Location(s): Tuesdays and Thursdays 2:20 pm – 3:35 pm ET @ Buckman
Hall 120
Credit Hours: 3
Vahid Behzadan, Ph.D. – Assistant Professor
Faculty Contact Information:
Office Location: Maxy120A or Zoom ( https://unewhaven.zoom.us/my/behzadan )
Phone: 203-479-4723 Email: vbehzadan@newhaven.edu
Office Hours: Tuesday & Thursday 12pm-1pm or by request
Department Chair: Dr. Ali Golbazi agolbazi@newhaven.edu
COURSE SYLLABUS:
This syllabus is informational in nature and is not an express or implied contract. It is subject to
change due to unforeseen circumstances, as a result of any circumstance outside the University’s
control, or as other needs arise. If, in the University’s sole discretion, public health conditions or
any other matter affecting the health, safety, upkeep or wellbeing of our campus community or
operations requires the University to make any syllabus or course changes or move to remote
teaching, alternative assignments may be provided so that the learning objectives for the course,
as determined by the University, can still be met. The University does not guarantee that this
syllabus will not change, nor does it guarantee specific in-person, on-campus classes, activities,
opportunities, or services or any other particular format, timing, or location of education, classes,
activities, or services.
Course Description:
Prerequisite: CSCI 6602 or equivalent course in Python.
An introduction to the fundamental methods of artificial intelligence (AI) used in problem solving.
Techniques include heuristic search, optimization, genetic algorithms, game playing, expert
systems, probabilistic reasoning, learning strategies, neural networks, natural language
understanding, image understanding. 3 credits.
This course will introduce the basic ideas and techniques underlying the design of intelligent
computer systems. A specific emphasis will be on the statistical and decision theoretic modeling
paradigm.
Course Structure/Course Format/Course Objectives: This class is offered an on-ground course, with in-person lectures, and in-person/online tutorial and discussion sessions, as well as written and online assignments, and programming projects. Active learning will constitue as much as 50% of the class. Participation will be recorded based on engagement in discussions (online/in-person), as well as submitted assignments. Course Objectives: By the end of this course, you will have built autonomous agents that efficiently make decisions in fully informed, partially observable and adversarial settings. Your agents will draw inferences in uncertain environments and optimize actions for arbitrary reward structures. Your machine learning algorithms will classify handwritten digits and photographs. The techniques you learn in this course apply to a wide variety of artificial intelligence problems and will serve as the foundation for further study in any application area you choose to pursue. Student Learning Outcomes: By the end of this course, students will be able to: 1. Build autonomous agents that efficiently make decisions in fully informed, partially observable and adversarial settings. 2. Create an agent that will draw inferences in uncertain environments and optimize actions for arbitrary reward structures. 3. Create a machine learning algorithm that will classify handwritten digits and photographs. Course Requirements & Assessment: Please see official University of New Haven Academic Policies located in the links below: Graduate Grading System
Assignments/Projects:
• All submissions are online, either via Canvas or Grade scope (as instructed in the
assignment details). Please turn in whatever you have for participation credit, event if
incomplete.
• Homework assignments can be completed via pen and paper, but the final submission must
be scanned/photographed copies of the work. If handwriting is deemed illegible there may
be a penalty, or the attempt may be completely reject.
Examinations:
• This course will have two exams: midterm and final. Both exams are take-home and openbook, but must be completed individually. The exams will include questions taken directly from the class discussions and exercises. Exams may also require handwritten code. Everything you are told or shown in class is fair game, not just the content of slides.
Participation:
Active learning will constitue as much as 50% of the class. Participation will be recorded based
on engagement in discussions (online/in-person), as well as submitted assignments.
Grading:
Grades earned are based on your performance on homework, quizzes, exams and the final exam.
Programming Assignments | 25% |
Electronic Homework Assignments | 15% |
Written Homework Assignments | 15% |
Final Project | 10% |
Midterm Exam | 10% |
Final Exam | 15% |
Total** | 100% |
**Final Grades are assigned with the following scale:
Typical Undergraduate Scale
Grades Scored Between | Letter Equivalent |
97 to 100 | A+ |
94 to Less than 97 | A |
90 to Less than 94 | A- |
87 to Less than 90 | B+ |
84 to Less than 87 | B |
80 to Less than 84 | B- |
77 to Less than 80 | C+ |
74 to Less than 77 | C |
70 to Less than 74 | C- |
67 to Less than 70 | D+ |
63 to Less than 67 | D |
60 to Less than 63 | D- |
Less than 60 | F |
total column in Canvas may or may not be reflective of your final grade.
Typical Graduate Scale
Grades Scored Between | Letter Equivalent |
97 to 100 | A+ |
94 to Less than 97 | A |
90 to Less than 94 | A- |
87 to Less than 90 | B+ |
84 to Less than 87 | B |
80 to Less than 84 | B- |
77 to Less than 80 | C+ |
74 to Less than 77 | C |
70 to Less than 74 | C- |
Less than 70 | F |
total column in Canvas may or may not be reflective of your final grade.
Expectations:
Students should expect to spend at least 3 hours on academic studies outside, and in addition to, each hour of class time. There will be readings, homework questions/problems, and programming projects. Attendance: Missing more than five lectures will result in an automatic “F” in the course (if you miss more than five lectures, then your course letter grade will be F). This policy may appear to be harsh, but please know that the aim of our attendance policy is by no means to add to your stress. The goal is to ensure that everyone is keeping up with the course. Many of us have the habit of procrastination. It has been repeatedly proven to me that it is less likely for my students to fall behind if they attend the lectures. Your education is of paramount importance and I care about you and your education.
• Note: If for reasons of illness, injury, or emergency health issues, you will not be able to regularly attend the lectures, you must email me by the end of Week 1. I will help you in any way I can. I promise together we will find an alternative method for recording your attendance.
• Note: If you know that you will not benefit from our strict attendance policy, please come talk with me during office hours by the end of Week 2. I will help you in any way I can. In particular, I can adjust the grading scale and create alternative midterm exams and a special comprehensive final exam for you if you do not want to regularly attend the lectures. But, if that is what you want, you must contact me by the end of Week 2.
Late Work: Assignments turned in late may be accepted with a grade penalty, if the solutions
are not distributed yet. This is completely at the discretion of the instructor, as the goal is to
balance learning and fairness.
Missed Work: Exams may be made up in only the most unavoidable situations (at the discretion
of the instructor). A formal excused absence (such as a note from Health Services or a healthcare
provider) will be required before you can make up a missed exam.
Individual Work: Students must work individually on assignments and projects unless
specifically allowed to work in groups by the instructor. Any work taken from the internet must
be cited properly (acceptance of code taken from elsewhere is at the discretion of the instructor)
or will be considered plagiarism. Failure to adhere to this policy will result in penalties ranging
from a zero on the assignment to a zero in the final grade. Students may also be subject to
disciplinary action by the University of New Haven (see University Policies).
TCoE Academic Lab reservation form
As a TCoE student, you have access to reserve academic lab spaces for academic purposes where
you need access to specific equipment. Example approved uses might include time for a team
meeting to finish a team project or a study-session with a TA. For more information or to submit
your reservation, please visit: https://forms.office.com/r/EUeJT36ZFr
Course Outline/Schedule:
Day/Date | Topic/Note |
Thursday, Jan 20 | Intro to AI for Cybersecurity |
Tuesday, Jan 25 | Landscape of cybersecurity – sources of data |
Thursday, Jan 27 | Introduction to machine learning – linear classifiers |
Tuesday, Feb 1 | SVM and Logistic Regression – Spam and Phishing Classification |
Thursday, Feb 3 | Clustering techniques for network anomaly detection |
Tuesday, Feb 8 | Machine learning for malware detection I – Intro to malware analysis |
Thursday, Feb 10 | Midterm Project |
Tuesday, Feb 15 | Machine learning for malware detection II |
Thursday, Feb 17 | Deep learning I – CNNs and RNNs |
Tuesday, Feb 22 | Deep learning II – GANs and deep fakes |
Thursday, Feb 24 | Paper Presentations – Final Project Proposal |
Tuesday, Mar 1 | Paper Presentations |
Thursday, Mar 3 | Adversarial Machine Learning – Paper Presentations |
Tuesday, Mar 8 | AI Safety, Security, and Ethics – Paper Presentations |
Thursday, Mar 10 | Final Project Presentations |
Tuesday, Mar 15 | Spring Break – No Class |
Thursday, Mar 17 | Spring Break – No Class |
Tuesday, Mar 22 | BNs – Representation |
Thursday, Mar 24 | BNs – Inference |
Tuesday, Mar 29 | BNs – Sampling |
Thursday, Mar 31 | Decision Networks, VPI |
Tuesday, Apr 5 | HMMs |
Thursday, Apr 7 | ML – Naïve Bayes |
Tuesday, Apr 12 | ML – Perceptron and Logistic Regression |
Thursday, Apr 14 | ML – Optimization and Neural Networks |
Tuesday, Apr 19 | ML – Neural Networks II |
Thursday, Apr 21 | Advanced Applications |
Tuesday, Apr 5 | Trustworthy AI: Safety, Security, Transparency |
Tuesday, Apr 5 | Project Presentations |
Tuesday, May 3 | Project Presentations |
Tuesday, May 5 | Final Review |
Tuesday, May 10 | Final Exam |
Diversity Statement
The University of New Haven embraces diversity and recognizes our responsibility to foster a
diverse, inclusive, and welcoming environment in which all members of the Charger community
of all backgrounds and identities can learn, work, and live together. We benefit from the academic, social, and cultural developments that arise from a diverse campus that is committed
to equity, inclusion, belonging, and accountability.
We have a responsibility as a community and as individuals to address and remove barriers, achieve success, and sustain a culture of inclusivity, empathy, kindness, and compassion. We encourage, welcome, and embrace participation in ongoing dialogue, engagement, and education to critically examine and thoughtfully respond to the changing realities of our community. Diversity, equity, inclusion, acceptance, and belonging enrich the Charger community and are instrumental to institutional success and fulfillment of the University mission.
Reporting Bias Incidents
At the University of New Haven, there is an expectation that all community members are committed to creating and supporting a climate which promotes civility, mutual respect, and open-mindedness. There also exists an understanding that with the freedom of expression comes the responsibility to support community members’ right to live and work in an environment free from harassment and fear. It is expected that all members of the University community will engage in anti-bias behavior and refrain from actions that intimidate, humiliate, or demean persons or groups or that undermine their security or self-esteem.
If you have an immediate safety concern for yourself or others, and/or believe someone poses an
immediate threat to themselves or others, please contact University Police at 203-932-7070 or call 911. Community members can report bias-motivated incidents by completing the form at www.newhaven.edu/biasreporting. Community members are encouraged to complete this form if they are the target of bias or harassing behaviors, witness such behaviors, or gain knowledge of these behaviors occurring within the University community. All matters concerning bias and harassment will be handled by the Dean of Students Office and Human
Resources Office.
University-wide Academic Policies
A continually-updated list of University-wide academic policies and descriptions of key university student resources, can be found on Canvas. You can access them by simply clicking on the (?) help button. The University-wide academic policies include (but are not limited to) the University’s attendance policy, procedures for both adding / dropping a course and course withdrawals, an explanation for the sorts of circumstances where incomplete (INC) grades could be considered by the faculty, and the academic integrity policy (among others). Also in this location you will find information regarding the process for reporting bias and topics related to our maintaining a positive learning environment (including, but not limited to, discrimination and sexual misconduct).
The list of key university student resources to enable learning include (but are not limited to) the University’s Center for Student Success, Writing Center, Center for Learning Resources, and the Accessibility Resource Center.
Course Delivery Options
• On-Ground: Fully on-ground course with every student meeting in-person
UNIVERSITY STUDENT SUPPORT SERVICES
The University recognizes that students can often use some help outside of class and offers academic assistance through several offices.
Accessibility Resources Center
Students with disabilities, chronic health-related conditions, or military service-connected disorders are encouraged to share, in confidence, information about course specific approved reasonable accommodations. The Accessibility Resources Center, located in Sheffield Hall, is responsible for and committed to providing supports and resources that serve to promote educational equity and ensure that students are able to participate in the opportunities available at the University of New Haven. Reasonable accommodations are not made without written documentation from the Accessibility Resources Center.
Center of Learning Resources(CLR)
The Center for Learning Resources (CLR), located in the Peterson Library, provides academic content support to the students of the University of New Haven using metacognitive strategies that help students become aware of and learn to apply optimal learning processes in the pursuit of creating independent learners. CLR tutors focus sessions on discussions of concepts and processes and typically use external examples to help students grasp and apply the material.
Center For Student Success (CSS)
The Center for Student Success provides students with a multitude of resources available on
campus and assists students in fulfilling their educational, social and personal goals.
Counseling & Psychological Services (CAPS)
CAPS offers confidential, free services in order to support student mental health and wellbeing. The services include individual and group therapy, support groups, consultations, and 24/7 crisis support. We are available in person and remotely, and are in the office M-F, 8:30-4:30. Please call us to schedule an appointment or with any questions at 203-932-7333; you can also schedule online. If you experience a mental health crisis after hours, you can call our main number for support.
Myatt Center
The Myatt Center for Diversity and Inclusion is committed to creating a multicultural environment through intentional education, campus community engagement, and valuing the unique identities of each member of the Charger Community. Our commitment to diversity is driven by the core values of connection, belonging, inclusivity, equity, acceptance, and accountability. The Myatt Center’s focus is to create a respectful and inclusive environment based our awareness and ability to engage with others who are different on many levels including ethnicity, race, sexual orientation, gender, military, religious belief, and life experiences.
Marvin K. Peterson Library
The Library provides access to online databases, e-books, e-journals, electronic U.S. Government Documents, print books, educational games, and audiovisual materials. A search can be conducted through all these resources at once by using the search box “Articles, Books, & More.” The Library provides three floors with individual quiet study space, collaborative group study space, study rooms with technology, whiteboards, Dell desktops, iMacs, scanners, and printers. The entire library is a wireless zone.
Librarians assist in locating relevant sources of information for research papers, thesis, honors thesis, and other projects. Librarians answer general reference questions and help with effectively evaluating sources of information. Help is available through a Chat Service, 24/7 Ask a Librarian Service, a Zoom Reference Service, and by E-Mail. Complete the Research Consultation Form to arrange a time convenient for you.
LibGuides are created to assist students with research. They contain an overview of resources
available through the library, as well as tutorials, subject guides, and course specific guides.
University Writing Center
The mission of the Writing Center is to provide high-quality tutoring to undergraduate and graduate students as they write for a wide range of purposes and audiences. Tutors are undergraduate and graduate students who are majoring in a variety of fields across the University. We are here to work with you at any stage in the writing process; just bring in your assignment, your ideas, and any writing you’ve done so far. To make an appointment, you can register for an account with our scheduling site
https://newhaven.mywconline.com or visit us in person at our desk on the first floor of Peterson Library (just to the left after you enter the library).