CS 335 – Introduction to Artificial Intelligence
This course covers strong artificial intelligence methods, which have resulted in the development of systems that solve open problems in specialized domains. Such methods include 1) AI models based on logical reasoning, in particular decision trees and learning decision trees, rule-based expert systems, semantic nets, and frames; 2) AI models based on probabilistic reasoning, in particular Bayesian networks and learning Bayesian networks, influence diagrams, and class probability trees; and 3) AI models based on emergent intelligence, in particular evolutionary computation and swarm intelligence. Lastly, the course discusses an important endeavor in AI, namely, language processing.
Course stats
- Course rating
- 4.9 / 5
- Recommendation
- 96% would recommend
- Instructor rating
- 4.9 / 5
Experience pulse
- Instructor Available for Help4.9 / 5
- Intellectually Challenging4.4 / 5
- I Learned a Lot4.8 / 5
Student reviews
Summer 2025
“The AI chatbot project was amazing, I gained hands-on experience training and deploying a model.”
Summer 2025
“Professor Omeed’s teaching style helped me thoroughly understand each topic.”
Logistics
- Semester
- Summer 2025
- Meeting times
- Tuesdays & Thursdays @ 11:00 AM - 12:50 PM (LWH 3003)
- Office hours
- Fridays @ 12:00 PM - 12:30 PM CST (Virtual) or by appointment
- Contact
- okadhams@neiu.edu
Major course topics
- Introduction to Artificial Intelligence (AI)
- Python Programming for AI
- Expert Systems
- Search and Problem Solving
- Machine Learning Fundamentals
- Natural Language Processing (NLP)
- Bayesian Inference and Probability
- Semantic Search and Vector Embeddings
- Retrieval-Augmented Generation (RAG)
- AI Ethics and Real-World Considerations
What to expect
- You will be able to understand what artificial intelligence (AI) is, including its history, core concepts, and modern applications.
- You can apply machine learning concepts such as supervised learning, training vs. testing data, and model evaluation.
- You can perform basic natural language processing (NLP) tasks, including tokenization, sentiment analysis, and text classification.
- Using the Retrieval-Augmented Generation (RAG) framework, you can build a chatbot that integrates information retrieval with text generation.
- You can collaborate with others using version control tools like GitHub and follow development best practices in AI projects.