Artificial Intelligence

Upon completion of this course, the student will be able to:

• explain what AI is and give examples of how AI is being used in the world. Have a basic understanding of what is inside AI and identify AI industry relevant applications.
• install Jupyter Notebook, create a notebook, name cells, run cells, create menus, add rich content, export notebooks, and use notebook extensions.
• explain what Machine Learning is and discuss its algorithms, techniques, and functions.
• demonstrate different methods used in creating data visualization using Tableau Public and how to communicate the results derived from the analysis of the data set.
• define deep learning and neural networks. Differentiate between learning and unsupervised learning and reinforcement learning.
• explain AI applications in generative AI, computer vision, and natural language processing, and provide an overview of their techniques and applications and why they are important.
• explain what AI ethics means and how to apply AI Ethics principles such as Human Rights, Bias, Inclusion, Privacy, Explainable AI, and Level of Autonomy.

Upon completion of this course, the student will be able to:

• describe different principles of ethical AI. These principles include but are not limited to human-centered AI, ensuring transparency, fairness, autonomy, beneficence, non-maleficence, privacy, etc.
• gain an understanding of human nature towards morality and ethical issues.
• examine common ethical pitfalls of AI and explore ways to avoid them.
• explain different approaches for designing ethical AI.

Upon completion of this course, the student will be able to:

• distinguish between Machine Learning (ML) and Deep Learning (DL).
• summarize and implement the mathematics behind the workings of AI using Python.
• implement different classification and regression ML models using Python.
• describe the mathematics behind the workings of a recommendation system.
• examine the working of different reinforcement learning models with the help of applications.
• students will be able to name and utilize an Artificial Neural Network (ANN) to solve a problem.
• outline different methods to overcome variance and bias in DL models.
• implement supervised DL models on the given datasets.
• structure the DL project according to the AI project cycle.
• attribute the efficiency of ML and DL models to the various emerging technologies.

Upon completion of this course, the student will be able to:

• students will be able to understand the basics of Natural Language Processing (NLP), types of NLP sets, and the process of data acquisition.
• students will be able to apply the steps involved in data curation process and understand data curation tools.
• students will understand the importance of data visualization in NLP and how to apply the data visualization techniques.
• students will be able to explore the working of popular text vectorisation methods and compare various vectorization techniques.
• students will be able to explore and apply the methods of document similarity and vector visualization using various distance measurement techniques.
• students will be able to describe and apply NLP classifiers to train machine learning models.
• students will be able to define Chatbots working, types and applications.

Upon completion of this course, the student will be able to:

• understand the basics of Computer Vision (CV), types, and the theory behind it.
• understand Data Acquisition for Computer Vision.
• understand Data Exploration.
• understand the basics of OpenCV, applications, functions, and implementation.
• learn about Computer Vision application, facial recognition and object detection.