Introduction to Artificial Intelligence Course
This course introduces students to the foundational concepts and techniques in artificial intelligence (AI).
Topics include problem-solving methods, knowledge representation, machine learning, and applications of AI in various domains.
Prerequisites
Basic programming skills
High school-level mathematics (algebra, probability)
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Week 1-2:
- • Introduction to Artificial Intelligence
- • Definition and scope of AI
- • Brief history of AI
- • AI applications and impact
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Week 3-4:
- • Problem Solving and Search Algorithms
- • Problem-solving agents
- • Uninformed search algorithms (e.g., depth-first search, breadth-first search)
- • Informed search algorithms (e.g., A* search)
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Week 5-6:
- • Knowledge Representation
- • Representation of knowledge in AI
- • Propositional and predicate logic
- • Semantic networks and frames
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Week 7-8:
- • Introduction to Machine Learning
- • Basic concepts of machine learning
- • Supervised learning vs. unsupervised learning
- • Regression and classification algorithms
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Week 9-10:
- • Machine Learning Techniques
- • Decision trees and ensemble methods
- • Neural networks and deep learning basics
- • Introduction to TensorFlow or scikit-learn for implementation
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Week 11-12:
- • Natural Language Processing (NLP)
- • Basics of NLP
- • Text preprocessing techniques
- • NLP tasks: sentiment analysis, part-of-speech tagging
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Week 13-14:
- • AI Applications and Ethical Considerations
- • AI applications in various domains (e.g., healthcare, finance, robotics)
- • Ethical considerations in AI development and deployment
- • Bias and fairness in AI systems
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Week 15:
- • Final Project and Review
- • Final project presentation
- • Review of course concepts
- • Course evaluation and feedback
Assesment:
