{
  "college": "laney-college",
  "edition": "2026-2027",
  "code": "AI",
  "name": "Artificial Intelligence",
  "sourceUrl": "https://lan.curriqunet.com/catalog/iq/44166/44231/44855/44990/44998",
  "courses": [
    {
      "code": "AI 6",
      "subject": "AI",
      "number": "6",
      "title": "Ethics and AI",
      "units": "3",
      "hours": "3 hours lecture",
      "cid": [],
      "transfer": [
        "CSU",
        "UC"
      ],
      "description": "Exploring the ethical implications and issues with artificial intelligence (AI) technologies and applications: Philosophical framework of ethical establishment encompassing human and machines, bias and system bias, privacy, transparency, explainable and responsible AI, societal impacts, AI ethics involving moral consideration of artificial people, emerging challenges while developing critical thinking skills to evaluate and address ethical concerns in AI development and deployment, and the future of work.",
      "notes": [],
      "offered": [
        {
          "term": "fall-2026",
          "name": "Fall 2026",
          "sections": 2,
          "open": 0,
          "url": "/c/laney-college/schedule/fall-2026/ai/#ai-6",
          "subject": "AI"
        }
      ]
    },
    {
      "code": "AI 20",
      "subject": "AI",
      "number": "20",
      "title": "Deep Learning (DL) I",
      "units": "3",
      "hours": "2 hours lecture, 4 hours lab",
      "prerequisite": "CIS 096F.",
      "advisory": "MATH 100.",
      "cid": [],
      "transfer": [
        "CSU",
        "UC"
      ],
      "description": "Introduction of fundamental concepts of Deep Learning (DL) as subset of Machine Learning (ML): Basic practice of DL and its applications, multi-layer neural network architectures in DL models, propagation algorithms, parameters, and collections of data and DL tools/libraries/packages.",
      "notes": [],
      "offered": []
    },
    {
      "code": "AI 22",
      "subject": "AI",
      "number": "22",
      "title": "Deep Learning (DL) II",
      "units": "3",
      "hours": "2 hours lecture, 4 hours lab",
      "prerequisite": "AI 020.",
      "cid": [],
      "transfer": [
        "CSU",
        "UC"
      ],
      "description": "Advanced Deep Learning (DL): Forefront technologies and techniques in testing and building applications through complex and multi-layered neural networks (NNs), generative adversarial networks (GANs), variational autoencoders (VAEs), deep reinforcement learning (DRL), and other transformers and unsupervising models.",
      "notes": [],
      "offered": [
        {
          "term": "fall-2026",
          "name": "Fall 2026",
          "sections": 2,
          "open": 2,
          "url": "/c/laney-college/schedule/fall-2026/ai/#ai-22",
          "subject": "AI"
        }
      ]
    },
    {
      "code": "AI 30",
      "subject": "AI",
      "number": "30",
      "title": "Applied Generative Artificial Intelligence (GenAI) I",
      "units": "3",
      "hours": "2 hours lecture, 4 hours lab",
      "prerequisite": "CIS 096F.",
      "advisory": "MATH 100.",
      "cid": [],
      "transfer": [
        "CSU",
        "UC"
      ],
      "description": "Fundamental concepts of Applied Generative Artificial Intelligence (GenAI) as part of AI technologies and application development: Basic practices of GenAI and its applications, large language models (LLMs), transformer architectures, retrieval-augmented generation (RAG), world of graphics processing units (GPUs) and neural processing units (NPUs), and hands-on GenAI application development.",
      "notes": [],
      "offered": []
    },
    {
      "code": "AI 32",
      "subject": "AI",
      "number": "32",
      "title": "Applied Generative Artificial Intelligence (GenAI) II",
      "units": "3",
      "hours": "2 hours lecture, 4 hours lab",
      "prerequisite": "AI 030.",
      "cid": [],
      "transfer": [
        "CSU",
        "UC"
      ],
      "description": "Advanced Generative Artificial Intelligence (GenAI) applications: Advanced GenAI applications in areas such as Healthcare and Autonomous Systems, forefront technologies and advanced techniques in testing and building combined GenAI and Retrieval-Augmented Generation (RAG) applications through complex and multi-language and data models, and applying advanced prompt engineering and concepts of federated learning (FL) in application development.",
      "notes": [],
      "offered": []
    }
  ]
}