Learning Outcomes for the Bachelor of Arts in Artificial Intelligence

The Bachelor of Arts degree in Artificial Intelligence allows students to develop the AI skills that employers are seeking, in machine learning, bot development, robotic process automation, and cognitive computing.

Learn by doing

Problem-solving is at the heart of this degree. Students at the IU School of Informatics and Computing at IUPUI collaborate on team-based, industry-specific design projects, creating new interfaces and business models for the Internet of Things. Your internship option allows you to work with our industry partners on projects related to AI.

You’ll discover AI as applied to human-centered computing, including industrial and business applications. This interdisciplinary degree covers all aspects of artificial intelligence, including AI for enterprise intelligence, human–AI interaction, AI design and development, and efficient AI solutions and applications.

Learning Outcomes

Human–AI Interaction

  • Design user interfaces to improve human–AI interaction and real-time decision-making.
  • Evaluate the advantages, disadvantages, challenges, and ramifications of human–AI augmentation.
  • Design and develop symbiotic human–AI systems that balance the information processing power of computational systems with human intelligence and decision making.
  • Explain the benefits, limitations, and tradeoffs of designing engaging and ethical conversational user interactions, including those supported by chatbots, smart speakers, and other AI-driven, voice-based technologies.
  • Design and evaluate conversational interfaces for different users and contexts of use.

AI Solutions & Applications

  • Develop systems that process unstructured, uncurated data automatically using artificial intelligence (AI) frameworks and platforms.
  • Determine the framework in which AI bots may function, including interactions with users and environments.
  • Design and implement cognitive automation for different industries.
  • Design AI frameworks and structures to integrate robotic process automation (RPA) in business process management systems (BPMS).

AI Design & Development

  • Analyze datasets with the following supervised learning methods: for functional approximation, multiple linear regression, splines, and local regression; for classification, logistic regression, linear discriminant analysis, decision trees, bagging, random forests, and boosting, and support vector machines.
  • Analyze datasets with the following unsupervised learning methods: for dimensionality reduction, principal components analysis; for grouping, k­means clustering and hierarchical clustering.
  • Explain the main concepts, models, technologies, and services of cloud computing, the reasons for the shift to this model, and its advantages and disadvantages.
  • Develop, install, and configure cloud-computing applications under software-as-a-service principles, employing Pig, Hive, and other cloud-computing frameworks and libraries.
  • Apply the MapReduce programming model to data analytics in informatics-related domains.
  • Evaluate the advantages and disadvantages of deep learning neural network architectures and other approaches in a case study.
  • Implement deep learning models in Python using the PyTorch and TensorFlow libraries and train them with real-world datasets.
  • Design recurrent neural networks with attention mechanisms for natural language classification, generation, and translation.
  • Implement information retrieval concepts and methods to return documents automatically based on user queries.
  • Write scripts and applications for the following: analysis of streaming data; document classification, comparison, and indexing; link analysis to rank web search results; information retrieval performance evaluation; web crawling.
  • Extract information from text automatically using concepts and methods from natural language processing (NLP), including stemming, n-grams, POS tagging, and parsing.
  • Develop speech-based applications that use speech analysis (phonetics, speech recognition, and synthesis).

AI & Organizational Intelligence

  • Analyze the implications of applying AI systems to organizations and future of work.
  • Explain how to develop AI systems to meet business, organizational, and technology requirements.
  • Implement AI frameworks and platforms to improve business, organizational, and technology outcomes.
  • Develop bots to automate organizational processes from end to end.
  • Create organizational intelligence using a holistic approach to enterprise systems based on business, organizational, and technology requirements.
  • Develop robotic process automation to manage business processes and to increase and monitor their efficiency and effectiveness.
  • Determine the framework in which artificial intelligence and the Internet of things may function, including interactions with people, enterprise functions, and environments.
  • Solve real-world problems in organizational processes and workflows by applying critical thinking, problem-solving, and cognitive computing skills.