Introduction to Artificial Intelligence and Machine Learning

GMUSD159

Credits: 2.00

Prerequisites : Basic understanding of programming concepts and mathematics

Course Duration : 30 Hours / 12 Weeks

Start Date : 2024-03-06

End Date : 2024-03-15

Seats Remaining : 60

Domain: Computer Science, Information Technology, and Engineering students interested in AI and ML

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Aim & Summary

The course covers the basics of AI, including problem-solving, knowledge representation, and reasoning, as well as the fundamentals of machine learning, such as supervised learning, unsupervised learning, and reinforcement learning. Participants will also learn about popular ML algorithms and tools used in data analysis and predictive modeling. Through practical exercises and case studies, participants will gain hands-on experience in implementing AI and ML techniques using popular frameworks and libraries. By the end of the course, participants will have a solid understanding of AI and ML concepts and be prepared to explore more advanced topics in these fields.

Course Content
Week 1	Introduction to Artificial Intelligence and Machine Learning
Week 2	Problem-Solving and Search Algorithms
Week 3	Knowledge Representation and Reasoning
Week 4	Introduction to Machine Learning: Types and Applications
QUIZ 1
Week 5	Supervised Learning: Linear Regression and Classification Algorithms
Week 6	Supervised Learning: Decision Trees and Ensemble Methods
Week 7	Unsupervised Learning: Clustering Algorithms
Week 8	Unsupervised Learning: Dimensionality Reduction Techniques
QUIZ 2
Week 9	Introduction to Neural Networks and Deep Learning
Week 10	Reinforcement Learning: Basics and Applications
Week 11	Machine Learning Tools and Frameworks (e.g., TensorFlow, scikit-learn)
Week 12	Case Studies and Applications of AI and ML
PROJECT REPORT

Course Content

Week 1

Introduction to Artificial Intelligence and Machine Learning

Week 2

Problem-Solving and Search Algorithms

Week 3

Knowledge Representation and Reasoning

Week 4

Introduction to Machine Learning: Types and Applications

QUIZ 1

Week 5

Supervised Learning: Linear Regression and Classification Algorithms

Week 6

Supervised Learning: Decision Trees and Ensemble Methods

Week 7

Unsupervised Learning: Clustering Algorithms

Week 8

Unsupervised Learning: Dimensionality Reduction Techniques

QUIZ 2

Week 9

Introduction to Neural Networks and Deep Learning

Week 10

Reinforcement Learning: Basics and Applications

Week 11

Machine Learning Tools and Frameworks (e.g., TensorFlow, scikit-learn)

Week 12

Case Studies and Applications of AI and ML

PROJECT REPORT

Course Certification

Certificate will have your name, photograph and the score in the final exam with the breakup. It will have the logos of GMU and company handling the course.