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Updated: May 5, 2023


About Course

The traditional problems (or goals) of AI research include reasoningknowledge representationplanninglearningnatural language processingperception, and the ability to move and manipulate objects

What you will learn?

  1. Solid understanding of the guiding principles of AI.

  2. Apply concepts of machine learning to real-life problems and applications.

  3. Design and harness the power of Neural Networks.

  4. Broad applications of AI in fields of robotics, vision, and physical simulation.

Course Syllabus

Class 1 - Introduction to Course

Class 2 - Introduction to AI

Class 3 - Structure of Agent

Class 4 - Types of Search Algorithms in AI

Class 5 - Introduction to Machine Learning

Class 6 - Supervised Machine learning

Class 7 - Unsupervised Machine Learning

Class 8 - Reinforcement learning

Class 9 - Introduction to Deep Learning

Class 10 - Difference between AI vs ML vs DL vs DS

Class 11 - Back Propagation Neural Network in AI

Class 12 - Expert System in AI

Class 13 - Text and Speech Recognition

Class 14 - Computer Vision - Seeing the World through AI

Class 15 - Bots - Conversation as a Platform

Class 16 - Next Steps of AI

To complete the course, the assessment is a marked quiz for each session that will contribute to your final grade, plus a final assessment called Assessment Scenarios at the end of the course. Assessment Scenarios involve reviewing 2 case studies/scenarios and answering questions related to basic concepts in the biomedical field.

Registration Link

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