AI Fundamentals: From Theory to Real-World Applications
From intelligent agents to machine learning, master foundational AI concepts and implement powerful algorithms to solve real-world challenges effectively.
From intelligent agents to machine learning, master foundational AI concepts and implement powerful algorithms to solve real-world challenges effectively.
Dive into the world of Artificial Intelligence with this comprehensive course from Columbia University. You'll explore the foundations of AI, from its historical roots to cutting-edge applications. Learn to design intelligent agents capable of solving real-world problems using techniques like search algorithms, game theory, logic, and constraint satisfaction. Gain hands-on experience by building a search agent and creating your own game. The course also introduces machine learning concepts, including linear regression. Perfect for those with a background in Python and probability, this advanced-level course will equip you with the skills to understand and apply AI across various domains, from self-driving cars to medical diagnostics.
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What you'll learn
Understand the history and fundamental concepts of Artificial Intelligence
Design and implement intelligent agents for various problem-solving tasks
Master search algorithms, including heuristic and adversarial search
Develop skills in game theory and its applications in AI
Learn the basics of machine learning, including linear models and neural networks
Explore logical reasoning and constraint satisfaction problems in AI
Skills you'll gain
This course includes:
Live video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 12 modules in this course
This course offers a comprehensive exploration of Artificial Intelligence, covering both theoretical foundations and practical applications. Students will delve into the history of AI before advancing to core concepts such as intelligent agents, search algorithms, and game theory. The curriculum includes hands-on programming exercises in Python, allowing students to implement AI techniques like search agents and game development. Machine learning fundamentals, including linear regression and neural networks, are introduced to provide a well-rounded understanding of AI capabilities. The course also touches on advanced topics like logical agents, constraint satisfaction problems, and real-world AI applications in natural language processing, robotics, and computer vision. By the end of the course, students will have gained a broad understanding of AI techniques and their applications in solving complex real-world problems.
Introduction to AI, history of AI, course logistics
Module 1
Intelligent agents, uninformed search
Module 2
Heuristic search, A algorithm
Module 3
Adversarial search, games
Module 4
Constraint Satisfaction Problems
Module 5
Machine Learning: Basic concepts, linear models, perceptron, K nearest neighbors
Module 6
Machine Learning: advanced models, neural networks, SVMs, decision trees and unsupervised learning
Module 7
Markov decision processes and reinforcement learning
Module 8
Logical Agent, propositional logic and first order logic
Module 9
AI applications (NLP)
Module 10
AI applications (Vision/Robotics)
Module 11
Review and Conclusion
Module 12
Fee Structure
Instructor
Pioneer in AI Applications for Healthcare and Education
Ansaf Salleb-Aouissi is a Lecturer in discipline at Columbia University's Computer Science Department, School of Engineering and Applied Science. After earning her BS in Computer Science from the University of Science and Technology (USTHB), Algeria in 1996, she completed her masters and Ph.D. in Computer Science from the University of Orleans, France, in 1999 and 2003 respectively. Her research expertise spans machine learning, artificial intelligence, medical informatics, and computer science, with significant contributions to healthcare and education technology. She has received notable recognition through a National Science Foundation award for her work on preterm birth prediction and a Pearson Education grant for advancing online self-learning research. Her scholarly work includes numerous publications in prestigious venues such as TPAMI, JMLR, ECML, PKDD, COLT, IJCAI, ECAI, and AISTAT. She is also known for co-authoring QuantMiner, an open-source software for mining quantitative association rules that has found widespread use in both professional and academic settings. Her work in machine learning applications for healthcare, particularly through the MUCMD (Machine Learning in Healthcare) platform, has significantly contributed to the field of medical informatics.
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