AIM 5000 - Artificial Intelligence
Graduate course, Yeshiva University, MS in Artificial Intelligence, 2022
Fall 2022
COURSE OVERVIEW
Artificial Intelligence (AI) is an interdisciplinary field, integrating knowledge and methods from computer science, mathematics, philosophy, psychology, economics, neuroscience, linguistics, and biology. Intelligent agents mimic cognitive functions to implement intelligent behaviors such as perception, reasoning, communication, and acting in symbolic and computational models. AI is used in a wide range of narrow applications, from medical diagnosis to speech recognition to bot control.
The autonomous single, multiple, and adversarial agents that students build in this course will support fully observable and partially observable decisions in both deterministic and stochastic environments. Topics covered include search, machine learning models, Markov decision processes, reinforcement learning, and deep learning in AI. The techniques and technologies mastered here will provide the foundational knowledge for the ongoing study and application of AI in other applications across practice areas.
COURSE LEARNING OUTCOMES
By the end of this course, students will be able to:
● Design and analyze autonomous agents that perceive and interact rationally with their environments.
● Describe, compare, and contrast the different knowledge representations, problem solving mechanisms, and learning techniques used in AI.
● Device and implement AI-based solutions to appropriate problems.
REQUIRED MATERIALS
Programming:
● Projects are to be completed and graded in Python. Basic programming skills in Python are required.
Required Texts:
● Norvig, Peter, and Russell, Stuart, Artificial Intelligence: A Modern Approach, 3rd edition (2016). Pearson.
Additional readings (either web-based or provided by the instructor) will be assigned.
ASSIGNMENTS & GRADING
| Assignments | Grading |
|---|---|
| Discussions / Response Assignments: Discussions will focus primarily on use cases, papers, podcasts, and presentations related to artificial intelligence. | 10% |
| Projects (3): Students will work individually and in teams on code-based “mini-projects” related to the current material. At the end of the course, each student will have a portfolio of relevant projects ready to show an employer. | 45% |
| Final Project Proposal, Project, Paperwork, and Presentation: Working individually or as part of a small team, students will build an artificial intelligence project that compares approaches to address a challenging and worthwhile problem of their choosing. Project evaluation criteria will include evaluation of (1) the statement of a compelling hypothesis or guiding question, (2) relevant choice of data sets(s), (3) comparative evaluation of different models, parameters, and hyper-parameters (4) findings and recommendations, and (5) a code notebook that is well-organized, clearly written, consistent with best practices, and reproducible. Students will present their final projects to their peers for feedback before submitting their final project write-up. The final presentation should be delivered within specified time parameters and include the appropriate level of detail for its intended audience. | 45% |
GRADING SCALE
| Quality of Performance | Letter Grade | Range % | GPA/ Quality Pts. |
|---|---|---|---|
| Excellent - work is of exceptional quality | A | 93 - 100 | 4 |
| A- | 90 - 92.9 | 3.7 | |
| Good - work is above average | B+ | 87 - 89.9 | 3.3 |
| Satisfactory | B | 83 - 86.9 | 3 |
| Below Average | B- | 80 - 82.9 | 2.7 |
| Poor | C+ | 77 - 79.9 | 2.3 |
| C | 70 - 76.9 | 2 | |
| Failure | F | < 70 | 0 |