Artificial Intelligence II
Explore advanced applications of AI, including machine learning and natural language processing.
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Course Description
- Design and implement advanced intelligent agents capable of solving complex real-world problems using machine learning, natural language processing, and computer vision techniques.
- Apply acquired knowledge to address challenging applications like self-driving cars, face recognition, and tumor detection.
- Gain a comprehensive understanding of the fundamental techniques for building intelligent computer systems through engaging learning activities, including quizzes and lecture videos.
- Establish a solid foundation for further studies in AI, equipping learners to tackle intricate AI problems with confidence.
Course Prerequisites
- Linear algebra (vectors, matrices, derivatives)
- Calculus
- Basic probability theory
- Python programming experience
What You Will Learn
By the end of this course, learners will be able to:
Demonstrate a comprehensive understanding of advanced techniques used to build intelligent computer systems, including constraint satisfaction problems, Markov decision processes, and reinforcement learning.
Apply essential machine learning techniques like decision trees, ensemble methods, logistic regression, and unsupervised learning to address real-world AI problems and challenges in natural language processing (NLP).
Gain a deeper understanding of AI and its practical applications, enhancing problem-solving skills for complex scenarios.
Prepare themselves for further studies or a career in AI, having acquired practical skills and a solid foundation in advanced AI concepts.
Course Outline
Module 1: Machine Learning 3
Module 2: Constraint Satisfaction Problems
Module 3: Reinforcement Learning
Module 4: Logical Agents
Module 5: AI Applications: NLP
Module 6: AI Applications and Course Review
Instructors
Dr. Ansaf Salleb-Aouissi's recent interdisciplinary research interests involve using advanced machine learning methods and large amounts of data to study medical problems, specifically premature birth and infantile colic. Alongside her research, Salleb-Aouissi is dedicated to education and is committed to advancing research on online self-learning and creating advanced tools for auto-grading, self-testing, and providing support to learners in computer science and mathematics. Salleb-Aouissi's research has been published in several high-quality peer-reviewed papers, including JMLR, TPAMI, ECML, PKDD, COLT, IJCAI, ECAI, and AISTAT. She joined the Department of Computer Science as a lecturer in discipline in July 2015 and holds a PhD in computer science from the University of Orleans, France, and received postdoctoral training at INRIA, Rennes (France). Salleb-Aouissi was appointed as an associate research scientist at the Columbia University’s Center for Computational Learning Systems in 2006, and she has also served as an adjunct professor with the Computer Science Department and the Data Science Institute in 2014 and 2015.
Please note that there are no instructors or course assistants actively monitoring this course.