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Applied Data Sciences

AI in Practice

Robotics: Creating machines that can perform complex tasks autonomously or semi-autonomously.

Autonomous Vehicles: AI-driven vehicles that operate without human input. They use AI to process sensory input, make decisions, and navigate safely in diverse conditions.

AI in Healthcare: AI for medical diagnosis, treatment planning, patient monitoring, and healthcare administration. 

AI in Finance: AI for algorithmic trading, fraud detection, risk management, and personalized financial planning. 

AI in Retail and E-commerce: AI for personalized shopping experiences, inventory management, price optimization, and customer service enhancements. 

AI in Manufacturing: AI for predictive maintenance, quality control, supply chain management, and production optimization. 

AI in Education: AI for personalized learning, automated grading, and learning analytics. 

AI in Marketing: AI for targeted advertising, customer segmentation, market analysis, and content personalization. 

AI in Agriculture: AI for crop monitoring, predictive analysis for farming, automated equipment, and optimizing agricultural supply chains. 

AI in Smart Cities: AI for optimizing urban services such as traffic management, energy utilization, waste management, and smart infrastructure maintenance. 

AI in Cybersecurity: AI for detecting and counteracting cyber threats, analyzing security risks, and improving overall cybersecurity measures. 

AI in Entertainment: AI for content recommendation, game development, music composition, and personalized streaming services. 

AI in Legal and Compliance: AI for legal document analysis, compliance monitoring, and automating routine legal tasks. 

AI in Environmental Science: AI for climate modeling, environmental monitoring, and analyzing ecological data.

AI Resources

AI Organizations 


  • Focus: Advanced AI research, large-scale machine learning models.
  • Website: OpenAI

DeepMind Technologies Limited

  • Focus: Neural networks, deep learning, AI for games and healthcare.
  • Website: DeepMind

Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory (MIT CSAIL)

  • Focus: Robotics, human-computer interaction, data science.
  • Website: MIT CSAIL

Stanford University Artificial Intelligence Lab (SAIL)

  • Focus: Robotics, deep learning, NLP, computer vision.
  • Website: Stanford AI Lab

Allen Institute for Artificial Intelligence (AI2)

  • Focus: NLP, machine reading, knowledge extraction.
  • Website: Allen AI

Partnership on AI to Benefit People and Society

AI Now Institute

  • Focus: AI's impact on society, rights and liberties.
  • Website: AI Now Institute

Google AI (Google)

  • Focus: Machine learning, quantum AI, AI for social good.
  • Website: Google AI

Meta AI (Facebook, Inc.)

  • Focus: Self-supervised learning, AI ethics, robotics.
  • Website: Facebook AI

IBM Research - Artificial Intelligence (IBM)

  • Focus: AI for business, blockchain, cloud computing.
  • Website: IBM AI Research

Microsoft Research - AI (Microsoft)

  • Focus: AI in healthcare, conversational AI, AI ethics.
  • Website: Microsoft AI

Baidu Research - Institute of Deep Learning (Baidu, Inc.)

  • Focus: Speech recognition, NLP, autonomous driving.
  • Website: Baidu Research

Berkeley Artificial Intelligence Research Lab (BAIR)

  • Focus: AI for creativity, human-compatible AI.
  • Website: BAIR

The Institute for Ethical AI & Machine Learning

  • Focus: Ethical frameworks, AI policy, and governance.
  • Website: Ethical AI
Educational Resources and Online Courses

Google AI Education




MIT OpenCourseWare



  • Kaggle Learn: Python, data visualization, machine learning, and deep learning.



Introductory Texts

  • Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig.
  • Life 3.0: Being Human in the Age of Artificial Intelligence by Max Tegmark.
  • Human Compatible: Artificial Intelligence and the Problem of Control by Stuart Russell.
  • Superintelligence: Paths, Dangers, Strategies by Nick Bostrom.
  • AI Superpowers: China, Silicon Valley, and the New World Order by Kai-Fu Lee.

Advanced Texts

  • Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.
  • Pattern Recognition and Machine Learning by Christopher Bishop.
  • Machine Learning: A Probabilistic Perspective by Kevin Murphy.
  • Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto.
  • The Hundred-Page Machine Learning Book by Andriy Burkov.
  • The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World by Pedro Domingos.

Specialized Topics

  • Python Machine Learning by Sebastian Raschka and Vahid Mirjalili
  • Natural Language Processing in Python by Steven Bird, Ewan Klein, and Edward Loper.
  • Deep Learning for Computer Vision by Rajalingappaa Shanmugamani.
  • Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play by David Foster.
  • Interpretable Machine Learning by Christoph Molnar - Focus on explainable AI.
  • The Singularity is Near: When Humans Transcend Biology by Ray Kurzweil.
  • Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy by Cathy O'Neil



Leading Academic Journals

  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • Expert Systems with Applications
  • Journal of Machine Learning Research
  • IEEE Transactions on Neural Networks and Learning Systems
  • Pattern Recognition
  • IEEE Transactions on Fuzzy Systems
  • Information Sciences
  • International Journal of Computer Vision
  • IEEE International Conference on Robotics and Automation
  • Fuzzy Sets and Systems
  • International Journal of Robotics Research
  • Neurocomputing
  • Pattern Recognition Letters
  • Journal of Memory and Language
  • Neural Networks

Specialized Journals

  • Journal of Robotics and Autonomous Systems
  • Journal of Natural Language Engineering
  • Data Mining and Knowledge Discovery
  • IEEE Computational Intelligence Magazine
  • IEEE Transactions on Cognitive Communications and Networking

LLM Card References

Did you pick up a Large Language Model Card in one of the AI Literacy Interest Group or Research Data and Digital Scholarship events?

Learn more about the details mentioned in the card, presented here:



Fundamental Concepts

Core Concepts of AI

  • Artificial Intelligence (AI)
  • Machine Learning (ML)
  • Deep Learning (DL)

Types of Machine Learning

  • Supervised Learning 
  • Unsupervised Learning 
  • Reinforcement Learning

Neural Networks and Architectures

  • Neural Networks
  • Convolutional Neural Networks (CNNs)
  • Recurrent Neural Networks (RNNs)
  • Generative Adversarial Networks (GANs)

Data Science and Analytics

  • Predictive Analytics
  • Data Mining
  • Feature Engineering
  • Dimensionality Reduction
  • Principal Component Analysis (PCA)

AI Techniques and Algorithms

  • Bayesian Networks
  • Decision Trees
  • Random Forests
  • Support Vector Machines (SVMs)
  • Gradient Descent
  • Backpropagation
  • Cross-Validation
  • Hyperparameter Tuning
  • Ensemble Learning
  • Optimization Techniques

AI in Perception and Understanding

  • Natural Language Processing (NLP)
  • Computer Vision
  • Speech Recognition
  • Semantic Analysis
  • Sentiment Analysis
  • Object Detection
  • Facial Recognition

AI Ethics and Governance

  • Algorithmic Bias
  • Ethical AI
  • AI Governance
  • AI Ethics and Law

Advanced and Emerging Topics in AI

  • Transfer Learning
  • Explainable AI (XAI)
  • Federated Learning
  • Quantum Computing in AI
  • Edge AI
  • Internet of Things (IoT) and AI
  • Robotics
  • Autonomous Systems
  • AI in Cybersecurity

AI Model Management

  • Overfitting and Underfitting
  • Model Generalization





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