EAS 595 -- Fundamentals of AI

Description

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 discuss the history and properties of basic AI systems. Topics include machine learning, neural network, and data science techniques. We will learn how to build basic machine learning models. We will discuss the challenges of bias, security, privacy, explainability, ethical issues, and the use of context. We will learn about AI’s use in applications such as image processing and computer vision, natural language processing, recommendation systems, and gaming. We will use Python as a prior coding language for all the class demos and assesments. The course will be a combination of lectures, discussions, activities and assignments that will prepare students without a computer science background to study and apply AI tools and applications in a variety of different domains.

Syllabus can be found here

Key Topics

• History of AI, Properties of AI Systems
• Introduction to Python
• Machine Learning (ML) Overview with Applications
• Data Visualization and Pre-processing
• Natural Language Processing (NLP)
• Recommendation System
• Security, Privacy, and Bias in AI
• Data Ethics
• Time Series Analysis with Python
• Reinforcement Learning (RL)

Grading Rubrics

Course Component	% of grade
Assignments [3 assignments: 15% + 15% + 10%]	40%
Final Project	20%
Weekly Quizzes	10%
Midterm I	15%
Midterm II	15%

Bonus Points

• Piazza Rockstar
• Jupyter Demo Time
• Candy Questions
• Poster Session Partiipation
• Other activities to be released as the course goes

Late Day Policy

• Students can use up to 5 free late days throughout the course that can be applied towards the assignments (some assignments may have a hard deadline)
• A late day extends the deadline by 24 hours If there is more than 5 days after the deadline, a penalty of 25% for one day will be applied to any work submitted after that time

Weekly Quizes - How does it work?

• Released every Tuesday 9:00am, due by Monday 11:59pm
• Can be found at UBlearns > Assignments
• Each quiz contains 3-5 problems on topics covered that week
• Quizzes come in various forms, including multiple choice, multiple answer, written and coding formats
• At the end of a submission, the system will give you your final score, unless it is in the written or coding format
• 11 quizzes in total, only 10 quizzes with the highest scores will be counted
• Three attempts are allowed, unless it is in the written or coding format

Prerequisites

None

Reference Materials

There is no official textbook for the class, but a number of the supporting readings will come from:

• Richard S. Sutton and Andrew G. Barto, "Reinforcement learning: An introduction", Second Edition, MIT Press, 2019 - is a classical book and covers all the basics
• Lecture slides, relevant papers, and other materials will be added in the table above

Additional references, that can be useful:

• Li, Yuxi. "Deep reinforcement learning." arXiv preprint arXiv:1810.06339 (2018). - an overview of the latest algorythms and applications in reinfocment learning
• David Silver's course on Reiforcement Learning

Usefull Tools:

• Overleaf (LaTex online document generator) - great tool for creating reports
• Google Colab (online Jupyter Notebook with free GPU) (link)

Academic Integrity Policy

Academic integrity is a fundamental university value. No collaboration, cheating, and plagiarism is allowed in projects, quizes, and the exam. Those found violating academic integrity will get an immediate F in the course.

1. Academic integrity is a fundamental university value.
2. No collaboration, cheating, and plagiarism is allowed in assignments, quizzes or the midterms.
3. The catalog describes plagiarism as “Copying or receiving material from any source and submitting that material as one’s own, without acknowledging and citing the particular debts to the source (quotations, paraphrases, basic ideas), or in any other manner representing the work of another as one’s own.”
4. Any suspicious cases will be officially reported using the Academic Dishonesty Report form and all bonus points will be subject to removal from the student’s final evaluation.
5. Those found violating academic integrity more than once throughout their program will receive an immediate F in the course.
6. Please refer to the UB Academic Integrity Policy for more details.

Academic Integrity is a very high priority not only for our Department, but the University as a whole. We are glad to provide you help to ensure you achieve great results during the course, however we are not tolerate any kind of cheating.

Hepfull Resourses

We want you to demonstrate your own achievements and showcase your own abilities during the course! From the course instructors side, we are glad to provide you all the help needed for you to succeed in the course. Here is some of the free resources provided by the University:

1. If you need help with English, check UB Writing Center
2. If you have issues with your device, the University provides access to computers, as well as equipment loans.
3. Your well-being is highly important, if you have any concerns, make sure to check Counseling Service.

Accessibility Resources

If you have a disability and may require some type of instructional and/or examination accommodation, please inform me early in the semester so that we can coordinate the accommodations you may need. If you have not already done so, please contact the Office of Accessibility Services, 60 Capen Hall, 645-2608, and also the instructor of this course. The office will provide you with information and review appropriate arrangements for accommodations. More details.

Diversity

The UB School of Engineering and Applied Sciences considers the diversity of its students, faculty, and staff to be a strength, critical to our success. We are committed to providing a safe space and a culture of mutual respect and inclusiveness for all. We believe a community of faculty, students, and staff who bring diverse life experiences and perspectives leads to a superior working environment, and we welcome differences in race, ethnicity, gender, age, religion, language, intellectual and physical ability, sexual orientation, gender identity, socioeconomic status, and veteran status.

FAQ

I want to prepare for the course, what can I do?

You can check our Python resources page and get familiar with Jupyter Notebooks and Python basics.

I am in the waiting list, can you help me to enrol?

Unfortunately there is nothing we can do at this time. I would suggest to keep an eye at the enrollment. Typically some students drop the course right before the drop-date deadline, so if your are in the waiting list, there is a high chance you will get enrolled, so I would strongly suggest to visit the lectures, before the enrolment is finilized, even if you are not registered at this time.

What programming language will be used?

We will be using Python (version >3.9) as the programming language for the projects.

Is attendance required?

Attendance is not required but is encouraged. Sometimes we may do in class exercises or discussions related to quizes or projects and these are harder to do and benefit from by yourself

I am highly interested in the course, can audit it?

Typically I welcome students interested in the topics to audit the course. Unfortunately this Fall our scheduled room is not big enough to fill all people interested. You are welcome to drop me an email one week after the class begins, I will give you updates if there is some space available.

Any suggestions or comments?

I would be glad to get a feedback from you, just send me an email.