Artificial Intelligence Lvl 1

• Module 1: Foundations of AI (6 credit hours)

• Introduction to AI: Definition, history, philosophy, and ethical considerations of AI.
• Problem-solving and Search: Search algorithms, heuristic search, game playing.
• Knowledge Representation and Reasoning: Logic, semantic networks, rule-based systems, probabilistic reasoning.

• Module 2: Machine Learning (12 credit hours)

• Supervised Learning: Regression, classification, model evaluation, overfitting, bias-variance trade-off.
• Unsupervised Learning: Clustering, dimensionality reduction, anomaly detection.
• Deep Learning: Neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), deep learning frameworks.

• Module 3: Advanced AI Topics (6 credit hours)

• Natural Language Processing (NLP): Text processing, sentiment analysis, machine translation, language models.
• Computer Vision: Image recognition, object detection, image segmentation.
• Reinforcement Learning: Markov decision processes, Q-learning, applications in robotics and game playing.

• Module 4: AI Applications and Project (6 credit hours)

• AI in Healthcare: Disease diagnosis, drug discovery, personalized medicine.

• Assessment:

• Assignments: Regular programming assignments to reinforce concepts and develop practical skills.
• Quizzes: Short quizzes to assess understanding of key concepts.
• Midterm and Final Exams: Comprehensive exams covering the entire course material.
• Capstone Project: Evaluation of the project's originality, technical implementation, and presentation.

• Learning Outcomes:

  Upon successful completion of this course, students will be able to:

• Understand the fundamental concepts and principles of AI.
• Apply various AI techniques to solve real-world problems.
• Implement AI algorithms using programming languages and tools.
• Evaluate and compare different AI approaches.
• Analyze the ethical and societal implications of AI.
• Communicate effectively about AI concepts and applications.

• This course structure provides a balanced mix of theoretical foundations, practical skills, and exposure to cutting-edge AI applications. The capstone project allows students to demonstrate their mastery of the subject matter and develop a portfolio-worthy project.

• AI in Finance: Fraud detection, algorithmic trading, risk management.
• AI in Robotics: Navigation, planning, control, human-robot interaction.
• Capstone Project: Students will work on a substantial AI project applying the concepts and techniques learned in the course.

30 hr

• Upon successful completion of this course, students will be able to:

• Understand the fundamental concepts and principles of AI.
• Apply various AI techniques to solve real-world problems.
• Implement AI algorithms using programming languages and tools.
• Evaluate and compare different AI approaches.
• Analyze the ethical and societal implications of AI.
• Communicate effectively about AI concepts and applications.