COMS 4701, Artificial Intelligence

Artificial intelligence (AI) is a broad, interdisciplinary, and rapidly evolving field of computer science concerned with the design of systems that can perceive, reason, learn, and act. This course provides an overview of the fundamental methods and applications of AI, with an emphasis on algorithms, computation, and the evolution of the field from classical approaches to modern AI systems. Topics include search and planning, probabilistic reasoning, machine learning, reinforcement learning, deep learning, foundation models, and agentic AI. Students will develop intuition and algorithmic thinking through written and programming problems in Python, and will apply these concepts to building and evaluating intelligent systems. By the end of the course, students will be able to formulate AI problems, reason and make decisions under uncertainty, learn from data, and design modern AI systems that use foundation models and other AI techniques.

Course Objectives

• Formulate and solve AI problems using state-space search.
• Model and solve adversarial and constraint-based problems using search and constraint satisfaction methods.
• Formulate sequential decision problems as Markov decision processes and apply dynamic programming and reinforcement learning methods.
• Model and perform probabilistic inference in uncertain and sequential environments.
• Apply fundamental machine learning methods to supervised learning problems.
• Explain and implement fundamental components of neural networks and deep learning.
• Explain the principles underlying transformers and foundation models.
• Design and evaluate modern AI systems.

Prerequisites

• Data structures and basic algorithms
• Programming experience, preferably in Python or a similar language
• Basic probability theory, including discrete probability distributions, conditional probability, independence, and Bayes’ rule
• Basic linear algebra, including vectors, matrices, matrix operations, and dot products
• Basic multivariable calculus (derivatives and gradients)

General List of Topics

1. Overview and history of AI
2. Intelligent agents
3. Uninformed and informed search
4. Local search and optimization
5. Constraint satisfaction problems
6. Games and adversarial search
7. Sequential decision making
8. Dynamic programming
9. Reinforcement learning
10. Monte Carlo tree search
11. Probability and Bayesian networks
12. Probabilistic inference
13. Sequential probabilistic models
14. Machine learning fundamentals
15. Supervised learning methods
16. Neural networks and representation learning
17. CNNs and computer vision
18. Attention and transformers
19. Foundation models and LLMs
20. Multimodal AI
21. Embeddings, retrieval, and RAG
22. Agentic AI
23. Evaluation, safety, and responsible AI