CORENG0101 - Introduction to Artificial Intelligence
Basic Information and Requirements
- Instructor: Prof. Neng-Fa Zhou
- Class hours: Friday 2:00PM - 6:00PM (1104 in the building at 17 Lexington Avenue)
- Reference Books and Websites:
Topics
The course covers several major topics in AI, including problem solving, search, logic, probabilistic reasoning, and machine learning. The focus is on algorithms and tools for implementing AI agents that receive percepts from the environment and perform actions in order to achieve some goal. This is a hands-on course, and students will explore the use of several high-level tools for solving AI problems. Students are also encouraged to implement search, reasoning, and machine learning algorithms.
Homework assignments
There will be one homework assignment every week. Unless notified otherwise, the homework is due in one week after it was assigned. Sample answers will be given and selected questions will be reviewed in class. There will be a one-point deduction for each missing homework or late submitted homework. The total deduction will not exceed 10 points.
Exams and Grading
There will be one midterm exam and one accumulative final exam, both closed-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)
- Solving Problems by Searching (Chapters 3,5)
- Constraint Solving and Planning with Picat
- Programming in Python
- Midterm Exam ( Sample)
- Probabilistic Reasoning
- Learning Decision Trees and Linear Regression
- Neural Networks and Deep Learning
- Natural Language Processing (NLP)
- Language models
- Using Spacy to perform several NLP tasks, including tokenization, sentence splitting, lemmatization, POS tagging, NER, parsing, text classification, sentiment analysis, and text summarization.
- Final Exam