What do self-driving cars, face recognition, web search, industrial robots, missile guidance, and tumor detection have in common?
They are all complex real world problems being solved with applications of intelligence (AI).
This course will provide a broad understanding of the basic techniques for building intelligent computer systems and an understanding of how AI is applied to problems.
You will learn about the history of AI, intelligent agents, state-space problem representations, uninformed and heuristic search, game playing, logical agents, and constraint satisfaction problems.
Hands on experience will be gained by building a basic search agent. Adversarial search will be explored through the creation of a game and an introduction to machine learning includes work on linear regression.
Introduction to Artificial Intelligence and intelligent agents, history of Artificial Intelligence
Building intelligent agents (search, games, logic, constraint satisfaction problems)
Machine Learning algorithms
Applications of AI (Natural Language Processing, Robotics/Vision)
Solving real AI problems through programming with Python
Week 1: Introduction to AI, history of AI, course logistics
Week 2: Intelligent agents, uninformed search
Week 3: Heuristic search, A* algorithm
Week 4: Adversarial search, games
Week 5: Constraint Satisfaction Problems
Week 6: Machine Learning: Basic concepts, linear models, perceptron, K nearest neighbors
Week 7: Machine Learning: advanced models, neural networks, SVMs, decision trees and unsupervised learning
Week 8: Markov decision processes and reinforcement learning
Week 9: Logical Agent, propositional logic and first order logic
Week 10: AI applications (NLP)
Week 11: AI applications (Vision/Robotics)
Week 12: Review and Conclusion
Students are required to have some basic of Python programming and an understanding of probability. Homework assignments will have a programming component in Python. The course offers an excellent opportunity for students to dive into Python while solving AI problems and learning its applications.
Linear algebra (vectors, matrices, derivatives)
Basic probability theory