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:

• explain the fundamentals of Python programming and the use of tools such as Colab and Jupyter Lab.
• explain AI data, datasets, and overall visualization techniques.
• practice data wrangling, augmentation, and imputation within Exploratory Data Analysis (EDA).
• practice data visualization with Python libraries/packages such as NumPy, Matplotlib, Seaborn, and others.
• discuss and analyze various techniques and results from data plotting and presentations.
• practice data analysis and training options with Pandas and Scikit-Learn.
• practice regression and classification using Scikit-learn, TensorFlow, and Keras.
• discuss future trends of analysis tool development, training/evaluation in AI applications, and visualization tools and techniques.

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.

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

• articulate and demonstrate Machine Learning (ML), Deep Learning (DL), and mathematical foundations of neural networks, including linear algebra and optimization.
• explain and demonstrate neural network basics in DL data acquisition and considerations.
• Implement and train feedforward and backpropagation neural networks using deep learning frameworks.
• apply tools and ML/DL libraries/packages such as Keras, Pytorch, TensorFlow; test and visualize with Matplotlib and Seaborn.
• practice convolutional neural networks (CNNs) and recurrent neural networks (RNNs) through perceptrons and hyperparameter tuning.
• practice generative adversarial networks (GANs), variational autoencoders (VAEs), and transformers for training deep learning networks.
• develop and demonstrate applications in image processing, robotics, NLP, and healthcare.

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

• explain the core principles of generative AI, including large language models (LLMs) and transformer architectures.
• apply and practice tools and frameworks relevant to GenAI concepts - Keras, PyTorch, TensorFlow.
• practice GANs, VAEs, Auto-Regressive models, transformers, and attention mechanisms.
• design and develop applications in chatbot, NLP, image processing, healthcare, and autonomous systems.
• evaluate generative AI technologies' ethical, social, and legal implications.

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

• define and explain advanced techniques in generative AI — GANs and VAEs.
• apply GAN architectures and systems to generative tasks, transferring learning, and domain adaptation.
• apply VAE structures and systems to generative tasks, transferring learning, and domain adaptation.
• research and explore cutting-edge AI tools, developments, and applications.
• apply reinforcement learning to generative tasks with multi-modal systems for the decision-making process.
• research advanced topics in Generative AI and its developments.
• assess and develop expertise in building and deploying large language models, eyeing future and emergent technologies along with ethical critique.

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

• explore and assess advanced deep learning architectures: Concepts, fundamentals, algorithms, and structures.
• practice and implement advanced neural network architectures (GANs, VAEs, transformers), including attention mechanisms and sparse deep learning.
• explain and implement recurrent neural networks for sequence modeling using techniques relevant to sequential data (RNN, LSTM, GRU).
• implement and improve models for NLP and CV tasks with embedded optimizations, including adaptive learning rates and advanced regularizations.
• explain and test reinforcement learning algorithms for decision-making tasks.
• develop strong problem-solving and critical thinking skills; stay updated with the latest deep learning and ethics trends.
• create and train multi-modal deep learning systems with underlined optimizations, training techniques, and deployment.
• design and conduct deep learning research experiments and collaboration with AI professionals, considering the future of deep learning development and potential career opportunities.

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

• design, train, and evaluate deep convolutional neural networks for image classification and recognition tasks.
• implement and apply state-of-the-art object detection and instance segmentation models (e.g., You Only Look Once (YOLO), Faster Region-based Convolutional Neural Network (Faster R-CNN), Mask Region-based Convolutional Neural Network (Mask R-CNN)).
• apply advanced image segmentation techniques, including semantic and panoptic segmentation.
• construct and use generative models such as Generative Adversarial Networks (GANs) and Variational Autoencoders for image synthesis and augmentation.
• analyze 3D vision concepts, including stereo vision, depth estimation, and point cloud processing.
• integrate optical flow, video analysis, and temporal modeling into computer vision pipelines.

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

• evaluate, fine-tune, and optimize pre-trained large language models (LLMs) such as GPT-4 and LLaMA for domain-specific NLP tasks and real-world applications.
• design and implement Retrieval-Augmented Generation (RAG) pipelines to enhance model accuracy and contextual relevance in knowledge-intensive applications.
• build and deploy production-grade NLP systems, including RESTful APIs and cloud-hosted inference services, applying best practices in scalability, performance, and monitoring.
• apply advanced techniques in prompt engineering, instruction tuning, and reinforcement learning from human feedback (RLHF) to align language models with specific user needs.
• develop multi-modal NLP applications that integrate text with other data modalities such as images or structured data, leveraging frameworks like LangChain or Hugging Face.