CSA 250 - Introduction to Artificial Intelligence

1. What is AI: Definition and history of AI, overview of weak vs strong AI concepts
2. AI Trends and Related Technologies: IoT, big data, 5G - how AI intersects and leverages these technologies
3. Main Domains of AI: In-depth look at natural language processing, computer vision, machine learning/statistical learning
4. AI Project Cycle: Steps involved in a typical AI project - data collection, data cleaning, model development, training, evaluation etc.
5. Ethical Considerations in AI: Algorithmic bias, transparency, privacy concerns and societal impacts of AI systems

1. Describe what is AI & what's not AI. Appreciate the history of AI and its evolution over time. (1)
2. Identify and analyze current trends in AI by correlating them with other technologies like IoT, Big Data & 5G. (2)
3. Identify 3 common domains of AI (Natural Language Processing, Computer Vision & Statistical Data) based on the type of underlying data. (3)
4. Examine and appreciate typical steps involved in an AI Project through the AI Project Cycle. (4)
5. Value ethical concerns around AI and examine the societal impact AI could have. (5)
6. Describe basic concepts & models encountered in Machine Learning & Deep Learning such as Supervised Learning, Unsupervised Learning, Neural Networks, Reinforcement Learning, etc. (3, 4)
7. Classify different kinds of data available into structured & unstructured based on the underlying quality of the dataset. Appreciate different roles each member of a typical Data Science team plays in a project. (2, 3, 4)
8. Examine common no-code tools available for AI Project Building and develop a use case using no-code tools in each domain of AI. (1, 3)
9. Discuss & interpret the future of AI, based on upcoming technological trends. (1, 2, 5)