CISC 3410 Artificial Intelligence
Basic Information and Requirements
- Instructor: Prof. Neng-Fa Zhou
- Class hours: MW 11:00-12:15PM 1141 IH
- Office hours: 1:05-2:05 Monday (room Ingersoll 1161)
- Reference Books and Web Sites:
Topics
This course provides a comprehensive introduction to artificial intelligence (AI) by integrating two major paradigms: symbolic AI, which emphasizes knowledge representation, reasoning, and search, and machine learning, which focuses on data-driven models and statistical learning. Students will explore the historical foundations of AI as well as the cutting-edge techniques driving modern applications. Topics include problem solving and heuristic search, logic-based knowledge representation and inference, planning, constraint solving, probabilistic reasoning, supervised and unsupervised learning, neural networks, deep learning, and reinforcement learning.
The course emphasizes both conceptual understanding and practical skills. Students will gain experience using classical algorithms (such as search, planning, and constraint satisfaction) and modern learning models (such as decision-tree learning and deep neural networks), while also examining how these approaches can complement each other in hybrid AI systems.
By the end of the course, students will be able to:
- Understand the strengths and limitations of symbolic and machine learning approaches.
- Apply key tools and algorithms from both paradigms to solve AI problems.
- Explore hybrid methods that combine symbolic reasoning with learning-based models.
Homework assignments
There will be one homework assignment each week. Unless otherwise announced, assignments are due one week after they are given. Please submit your homework by email to nzhou (AT) brooklyn.cuny.edu, with your name, student ID, and assignment number in the subject line. Include both your written solutions and code in the body of the email.
Sample solutions to programming problems will be provided, and selected questions will be reviewed in class. A one-point penalty will be applied for each missing or late submission, with total deductions capped at 10 points.
Exams and Grading
There will one midterm exam and one accumulative final exam, both open-book. The midterm accounts for 30%, the final accounts for 40%, and the remaining 30% of the grade will be based on homework, quizzes, and projects.
Course Outline
- Introduction (Chapter 1)
- AI Programming in Picat
- Solving Problems by Searching (Chapters 3)
- Adversarial search, games
- Midterm Exam ( Sample) (October 22, Wednesday)
- Uncertainty and probability
- Bayesian Networks
- Learning Decision Trees and Linear Regression
- Neural Networks and Deep Learning
- Natural Language Processing (NLP) and LLMs
- Final Exam