MSCS_AIE 531: Introduction to Artificial Intelligence
Categories: MS in Computer Science
About Course
Artificial Intelligence (AI) is a rapidly evolving field that enables computer systems to perform tasks traditionally requiring human intelligence, including learning, reasoning, perception, decision-making, and language understanding. This course introduces students to the fundamental concepts, methodologies, and applications of artificial intelligence based on the principles presented in Artificial Intelligence: A Modern Approach (4th Edition) by Stuart Russell and Peter Norvig.
Students will explore the evolution of AI, intelligent agents, search algorithms, knowledge representation, machine learning, neural networks, probabilistic reasoning, natural language processing, robotics, AI ethics, and industry applications. The course emphasizes both theoretical foundations and practical problem-solving approaches used in modern AI systems.
Through case studies, algorithmic analysis, and project-based activities, learners will develop the ability to evaluate AI techniques, design intelligent solutions, and understand the challenges associated with responsible AI development and deployment.
What Will You Learn?
- Define the fundamental concepts, history, and evolution of artificial intelligence.
- Identify major AI applications across healthcare, automation, finance, and decision-making systems.
- Explain different AI search strategies and evaluate their performance.
- Analyze knowledge representation and reasoning approaches used in intelligent systems.
- Apply machine learning techniques for classification, prediction, and decision-making tasks.
- Explain neural network architectures and deep learning methodologies.
- Apply probabilistic reasoning techniques to manage uncertainty in AI systems.
- Develop an understanding of natural language processing and language-based AI applications.
- Evaluate AI-based robotics, perception systems, and autonomous technologies.
- Analyze ethical challenges including bias, privacy, fairness, and responsible AI.
- Examine industry applications and evaluate practical AI solutions.
- Explore emerging AI technologies including generative AI and reinforcement learning.
- Develop and evaluate an AI-based project using appropriate algorithms and methodologies.
Course Content
Week 1: Introduction to Artificial Intelligence
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Week 1: Introduction to Artificial Intelligence
00:00 -
LO1: Define the fundamental concepts, history, and evolution of artificial intelligence.
00:00 -
LO2: Identify major applications of AI across different domains, including healthcare, automation, and decision-making.
00:00 -
LO3: Describe the role of artificial intelligence in solving real-world problems.
00:00 -
Multiple Choice Questions
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True/False
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Scenario Based Multiple Choice Questions
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Key Terms and Concepts Questions
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Short Answer Questions
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Written Assignment
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Presentation Task
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Role-Playing Activity
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Peer Review Task
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Exercises and Activities Adaptation
Week 2: Search Algorithms
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Week 2: Search Algorithms
00:00 -
LO1: Explain the principles of uninformed search, informed search, and optimization algorithms.
00:00 -
LO2: Apply search techniques to solve computational problems using artificial intelligence approaches.
00:00 -
LO3: Compare different search algorithms based on efficiency, completeness, and solution quality.
00:00 -
Multiple Choice Questions
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True/False
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Scenario Based Multiple Choice Questions
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Key Terms and Concepts Questions
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Short Answer Questions
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Written Assignment
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Presentation Task
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Role-Playing Activity
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Peer Review Task
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Exercises and Activities Adaptation
Week 3: Knowledge Representation and Reasoning
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Week 3: Knowledge Representation and Reasoning
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LO1: Describe knowledge representation methods, including logic, ontologies, and rule-based systems.
00:00 -
LO2: Demonstrate reasoning mechanisms used in knowledge-based artificial intelligence systems.
00:00 -
LO3: Analyze different approaches for representing and organizing knowledge in intelligent systems.
00:00 -
Multiple Choice Questions
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True/False
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Scenario Based Multiple Choice Questions
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Key Terms and Concepts Questions
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Short Answer Questions
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Written Assignment
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Presentation Task
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Role-Playing Activity
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Peer Review Task
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Exercises and Activities Adaptation
Week 4: Machine Learning Fundamentals
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Week 4: Machine Learning Fundamentals
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LO1: Identify the characteristics of supervised learning, unsupervised learning, and reinforcement learning approaches.
00:00 -
LO2: Utilize machine learning techniques for classification, prediction, and decision-making tasks.
00:00 -
LO3: Evaluate the effectiveness of different learning methods for specific applications.
00:00 -
Multiple Choice Questions
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True/False
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Scenario Based Multiple Choice Questions
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Key Terms and Concepts Questions
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Short Answer Questions
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Written Assignment
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Presentation Task
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Role-Playing Activity
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Peer Review Task
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Exercises and Activities Adaptation
Week 5: Neural Networks and Deep Learning
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Week 5: Neural Networks and Deep Learning
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LO1: Explain the structure and components of neural networks and deep learning models.
00:00 -
LO2: Illustrate the training process of neural networks using backpropagation techniques.
00:00 -
LO3: Assess the suitability of deep learning approaches for solving complex AI problems.
00:00 -
Multiple Choice Questions
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True/False
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Scenario Based Multiple Choice Questions
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Key Terms and Concepts Questions
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Short Answer Questions
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Written Assignment
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Presentation Task
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Role-Playing Activity
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Peer Review Task
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Exercises and Activities Adaptation
Week 6: Probabilistic Reasoning
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Week 6: Probabilistic Reasoning
00:00 -
LO1: Recognize the concepts of Bayesian networks, Markov decision processes, and probabilistic inference.
00:00 -
LO2: Apply probabilistic reasoning methods to represent uncertainty in artificial intelligence systems.
00:00 -
LO3: Examine the contribution of probabilistic models in AI-based decision-making.
00:00 -
Multiple Choice Questions
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True/False
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Scenario Based Multiple Choice Questions
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Key Terms and Concepts Questions
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Short Answer Questions
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Written Assignment
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Presentation Task
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Role-Playing Activity
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Peer Review Task
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Exercises and Activities Adaptation
Week 7: Natural Language Processing
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Week 7: Natural Language Processing
00:00 -
LO1: Describe language models, parsing methods, and semantic analysis techniques used in NLP.
00:00 -
LO2: Demonstrate techniques for processing and interpreting human language data.
00:00 -
LO3: Investigate challenges associated with developing effective natural language processing systems.
00:00 -
Multiple Choice Questions
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True/False
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Scenario Based Multiple Choice Questions
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Key Terms and Concepts Questions
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Short Answer Questions
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Written Assignment
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Presentation Task
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Role-Playing Activity
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Peer Review Task
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Exercises and Activities Adaptation
Week 8: Midterm Test
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Week 8: Midterm Test
00:00 -
Multiple Choice Questions
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True/False
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Scenario Based Multiple Choice Questions
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Short Answer Questions
Week 9: Robotics and Perception
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Week 9: Robotics and Perception
00:00 -
LO1: Explain the fundamental concepts of robotics, computer vision, and perception systems.
00:00 -
LO2: Implement AI-based perception techniques for interpreting information from robotic environments.
00:00 -
LO3: Evaluate the role of perception systems in developing autonomous robotic applications.
00:00 -
Multiple Choice Questions
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True/False
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Scenario Based Multiple Choice Questions
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Key Terms and Concepts Questions
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Short Answer Questions
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Written Assignment
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Presentation Task
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Role-Playing Activity
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Peer Review Task
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Exercises and Activities Adaptation
Week 10: Ethics and AI
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Week 10: Ethics and AI
00:00 -
LO1: Identify ethical concerns associated with artificial intelligence development and deployment.
00:00 -
LO2: Analyze issues related to algorithmic bias, privacy, fairness, and social impact.
00:00 -
LO3: Assess approaches for responsible design and implementation of AI systems.
00:00 -
Multiple Choice Questions
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True/False
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Scenario Based Multiple Choice Questions
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Key Terms and Concepts Questions
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Short Answer Questions
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Written Assignment
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Presentation Task
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Role-Playing Activity
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Peer Review Task
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Exercises and Activities Adaptation
Week 11: AI in Industry
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Week 11: AI in Industry
00:00 -
LO1: Outline applications of artificial intelligence in healthcare, finance, and autonomous systems.
00:00 -
LO2: Examine industry case studies to determine AI benefits and implementation challenges.
00:00 -
LO3: Evaluate the effectiveness of AI solutions in addressing practical industry requirements.
00:00 -
Multiple Choice Questions
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True/False
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Scenario Based Multiple Choice Questions
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Key Terms and Concepts Questions
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Short Answer Questions
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Written Assignment
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Presentation Task
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Role-Playing Activity
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Peer Review Task
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Exercises and Activities Adaptation
Week 12: Advanced AI Techniques
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Week 12: Advanced AI Techniques
00:00 -
LO1: Summarize advanced AI techniques, including reinforcement learning, generative models, and complex problem-solving approaches.
00:00 -
LO2: Develop AI-based solutions using advanced computational techniques.
00:00 -
LO3: Analyze the capabilities and limitations of advanced artificial intelligence methodologies.
00:00 -
Multiple Choice Questions
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True/False
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Scenario Based Multiple Choice Questions
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Key Terms and Concepts Questions
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Short Answer Questions
Week 13: Future of AI
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Week 13: Future of AI
00:00 -
LO1: Review emerging trends and developments influencing the future of artificial intelligence.
00:00 -
LO2: Explore challenges related to AI safety, reliability, and responsible innovation.
00:00 -
LO3: Evaluate the potential impact of future AI technologies on society and research.
00:00 -
Multiple Choice Questions
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True/False
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Scenario Based Multiple Choice Questions
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Key Terms and Concepts Questions
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Short Answer Questions
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Written Assignment
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Presentation Task
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Role-Playing Activity
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Peer Review Task
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Exercises and Activities Adaptation
Week 14: AI Project Development
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Week 14: AI Project Development
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LO1: Outline the stages involved in planning and developing an AI-based project.
00:00 -
LO2: Construct an AI solution by applying appropriate algorithms and development techniques.
00:00 -
LO3: Measure project outcomes based on accuracy, performance, and practical relevance.
00:00 -
Multiple Choice Questions
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True/False
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Scenario Based Multiple Choice Questions
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Key Terms and Concepts Questions
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Short Answer Questions
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Written Assignment
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Presentation Task
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Role-Playing Activity
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Peer Review Task
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Exercises and Activities Adaptation
Week 15: Review and Final Preparation
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Week 15: Review and Final Preparation
00:00 -
LO1: Summarize key concepts related to AI algorithms, applications, and ethical considerations.
00:00 -
LO2: Apply artificial intelligence principles to solve representative course-related problems.
00:00 -
LO3: Assess major AI approaches and their applications for final course preparation.
00:00 -
Multiple Choice Questions
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True/False
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Scenario-Based Multiple Choice Questions
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Key Terms and Concepts Questions
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Short Answer Questions
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Written Assignment
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Presentation Task
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Role-Playing Activity
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Peer Review Task
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Exercises and Activities Adaptation
Week 16: Final Test
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Week 16: Final Test or Project
00:00 -
Multiple Choice Questions
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True/False
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Scenario Based Multiple Choice Questions
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Short Answer Questions
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Written Assignment
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Presentation Task
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Role-Playing Activity
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Peer Review Task
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Exercises and Activities Adaptation
