Updated: May 5
The traditional problems (or goals) of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception, and the ability to move and manipulate objects
What you will learn?
Solid understanding of the guiding principles of AI.
Apply concepts of machine learning to real-life problems and applications.
Design and harness the power of Neural Networks.
Broad applications of AI in fields of robotics, vision, and physical simulation.
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.