EAS595 - Fundamentals of Artifical Intelligence
MW 6:30-7:50PM
113A Davis Hall
Course Syllabus
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Instructor: |
Dr. David Doermann |
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Office: |
113M Davis Hall |
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Email: |
Prefers to be contact through Piazza |
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Office Hours: |
Thursday 11:30AM-1:30PM, or by appointment |
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Instructor: |
Mihir Chauhan |
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Office Hour Location: |
302/TA Area Davis Hall |
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Email: |
Prefers to be contact through Piazza |
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Office Hours: |
Wednesday: 11am to 12pm |
Lectures, Homeworks, Quizzes and Projects during the 14-week semester.
This course is intended for Engineering graduate students who are interested in understanding the fundamental issues, challenges and techniques that are associated with recent advances in Artificial Intelligence (AI). The course will take a deep dive into the history and properties of AI systems, the challenges of bias, security, privacy, explain ability and the use of context. We will discuss social and ethical issues and discuss AIs use in applications such as image processing and computer vision, gaming and autonomy and robotics. The course is supported by a primer on the use of Python, and to a limited extent Matlab, to support home works and projects related to machine learning. The course will be a combination of lectures, discussions, activities and projects that will prepare students without a computer science background to study and apply artificial intelligence tools and applications in a variety of different domains. The course will have a midterm and final exam, regular homeworks, quizzes and three projects that will all be supported by the basic lecture material.
The course is not intended for students who have extensive machine learning, computer science or Python programming backgrounds. Undergraduates who wish to take this course and petition for credit by inquiring with the SEAS graduate office
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History of AI, Properties of AI Systems |
Challenge: Explainability |
Application: Games |
General Programming Trends in AI |
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Machine Learning Overview |
Use of Context in AI |
Application: Autonomy and Robotics |
Privacy in Machine Learning |
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Challenge: Bias |
Social and Ethical Issues |
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Python Libraries - NLP |
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Challenge: Security |
Applications: Natural Language Processing |
Introduction to WEKA |
Python Libraries - Keras |
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Challenge: Privacy |
Application: Image Analysis |
Introduction to Python |
Reinforcement Learning Python |
Textbook: None
- Class attendance and participation is expected
- You are responsible for ALL materials presented in class and assigned to read
- Quizzes will be given during class time only.
- There will be three projects
- Regular deliverables on the project will be graded during the course
- All assignments will be graded out of 100 points and weighted according to the table below
- I will drop your lowest quiz and lowest homework grade.
- All assignments will be turned in via UB Learns
- Quizzes and tests will be given online through the UB Learns system. You must install Respondus to take quizzes and tests.
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Weighting |
Assessment / Assignment |
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15% |
Homeworks |
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15% |
Quizzes |
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25% |
Projects |
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20% |
Midterm |
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25% |
Final |
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100% |
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Late Submission Policy
- Completed homework and project deliverables are to be submitted by their deadline (11:59pm).
- For homework, you will have up to 3 days to receive a grade reduced by 50%. No additional late days allowed
- Projects: You will be allowed a total of 3 days/partial day late submissions throughout the semester.
- For homework, each late day will reduce your grade by 50%, and for projects each late day beyond the 3 allowed will reduce your grade by 50%.
- No individual project/homework will be accepted after 3 days late.
Regrading Policy to Correct Grading Errors
- Assignments, quizzes and exams may be submitted for regrading to correct grading errors.
- Regrade requests are due no later than one (1) week after the scores are posted.
- Regrade requests must be clearly written and attached to the assignment.
- When work is submitted for regrade, the entire work may be regraded, which may result in a lower grade.
- Work done in pencil may not be considered for regrading.
Exam Policy
- No makeup exams will be given except in provably extreme circumstances.
- Notify your instructor 24 hours prior to the exam via e-mail if you are going to miss it. If it is medically impossible for you to give prior notice, please obtain a note from a physician detailing the period (and the reason) you were medically incapable of communicating with the instructor.
- If you miss an exam/quiz because of sickness or similar reasons, visit a physician and obtain a note detailing the period and the reason you were medically incapable of taking the exam/quiz.
- You are responsible for knowing about the exam date. Please plan your travel and other activities accordingly.
- If your schedule results in 3 exams on the same day, you may request a reschedule. This must be done at the beginning of the semester.
Grading Policy
- No "I" (Incomplete) will be given except under provably extreme circumstances.
- There is no grade negotiation at the end of the semester.
Disabilities
- If you have a diagnosed disability (physical, learning, or psychological) that will make it difficult for you to carry out the course work as outlined, or that requires accommodations such as recruiting note-takers, readers, or extended time on exams or assignments, please advise the instructor during the first two weeks of the course so that we may review possible arrangements for reasonable accommodations. In addition, if you have not yet done so, contact the Office of Disability Services.
- All work must be your own
- Do not take the answers, words, ideas or research findings of other people as yours; cite and acknowledge properly, and develop your own ideas.
- No cheating
- According to departmental policy, any violation of academic integrity will result in a Failing Grade for the course, and termination of departmental financial scholarship.
- Tools will be used to check similarity. Similar submissions will result in a Failing Grade for all involved parties.
Use of a code from an online repository, e.g. Github, must include a proper and clearly visible attribution in your report.