Machine Reasoning
In today's competitive landscape, leveraging advanced reasoning systems is essential for businesses aiming to optimize operations and make informed decisions. Machine reasoning offers a sophisticated approach to mimicking human thought processes, enabling machines to reason, learn, and act autonomously. This course provides you with the knowledge and skills to design and implement reasoning systems that can handle complex problems and enhance decision-making capabilities. By integrating logical inference and knowledge representation with big data and machine learning, new opportunities for innovation and strategic growth can be uncovered.
$4,500/person
Cost
Program Length
4 Days
This training is proudly affiliated with the National University of Singapore Institute of Systems Science (NUS-ISS), a global leader in education and research. Learn more about our partnership »
Course Overview
Course Content
Introduction to Reasoning Systems: Understand the evolution from human to machine intelligence and the fundamentals of AI applications in reasoning.
Architectures of Reasoning Systems: Explore the structures and cognitive functions of reasoning systems, including learning, perceiving, and acting.
Knowledge Representation Techniques: Learn essential techniques for representing and acquiring knowledge, building, and utilizing knowledge bases.
Deductive Reasoning and Logical Inference: Develop proficiency in deductive reasoning and logical inference, constructing and applying logical models.
Reasoning Under Uncertainty: Study methods for handling uncertainty, including probabilistic reasoning and decision-making under various conditions.
Contemporary Reasoning Systems: Master the use of modern reasoning systems that integrate big data and machine learning for knowledge discovery and problem-solving.
You will gain practical experience through scenario-based case studies and hands-on sessions using popular tools and frameworks.
Key Takeaways
Comprehensive Understanding of Machine Reasoning: Grasp the evolution of machine reasoning, its applications, and the architecture of reasoning systems.
Knowledge Representation: Learn techniques for representing and acquiring knowledge, essential for enabling machine reasoning.
Deductive and Inductive Reasoning: Develop skills in deductive reasoning and logical inference, constructing and applying logical models to solve complex problems.
Handling Uncertainty: Understand methods for reasoning under uncertainty, including probabilistic reasoning and decision-making under various conditions.
Integration with Big Data and Machine Learning: Discover how contemporary reasoning systems leverage big data and machine learning for knowledge discovery and problem-solving.
Prerequisites
Competent in Python programming.
Experienced in using Jupyter Notebooks, Google Colab, and well-versed in package installation.
Course Logistics
No Printed Materials: Course materials are accessed digitally. Do kindly note that no printed copies of course materials will be issued.
Device Requirements: Bring an internet-enabled device (laptop, tablet, etc.) with power chargers to access and download course materials.
For cancellation notices received more than fifteen business days prior to the class date, students may receive either a full refund or reschedule into another class date.
For cancellation notice less than fifteen business days prior to the class start date, students will receive an voucher in the amount of the paid tuition to use for the same course up to a six months’ time frame and will be automatically put onto the wait list of the course of their choice and granted final admission ten business days prior to class start day based on enrollment levels.