{"id":7201,"date":"2019-12-14T16:52:13","date_gmt":"2019-12-14T16:52:13","guid":{"rendered":"https:\/\/www.inspirenignite.com\/vtu\/neural-networks-and-ai-in-biomedical-engineering-med-elec-8th-sem-syllabus-for-vtu-be-2017-scheme\/"},"modified":"2019-12-14T16:52:13","modified_gmt":"2019-12-14T16:52:13","slug":"neural-networks-and-ai-in-biomedical-engineering-med-elec-8th-sem-syllabus-for-vtu-be-2017-scheme","status":"publish","type":"post","link":"https:\/\/www.inspirenignite.com\/vtu\/neural-networks-and-ai-in-biomedical-engineering-med-elec-8th-sem-syllabus-for-vtu-be-2017-scheme\/","title":{"rendered":"Neural Networks and Ai in Biomedical Engineering Med Elec 8th Sem Syllabus for VTU BE 2017 Scheme"},"content":{"rendered":"

Neural Networks and Ai in Biomedical Engineering detail syllabus for Medical Electronics (Med Elec), 2017 scheme is taken from VTU<\/a> official website and presented for VTU students. The course code (17ML82X), and for exam duration, Teaching Hr\/week, Practical Hr\/week, Total Marks, internal marks, theory marks, duration and credits do visit complete sem subjects post given below.<\/p>\n

For all other med elec 8th sem syllabus for be 2017 scheme vtu you can visit Med Elec 8th Sem syllabus for BE 2017 Scheme VTU Subjects<\/a>. The detail syllabus for neural networks and ai in biomedical engineering is as follows.<\/p>\n

Module 1 <\/h4>\n

ARTIFICIAL NEURAL NETWORK: What is an artificial neural network, Benefits, model of a neuron, Types of activation function, neural networks viewed as directed graphs, architectural graph of a neuron with feedback, Network Architectures, Artificial intelligence and Neural Networks. Learning Processes: Learning in context to neural Networks, learning paradigms, supervised & unsupervised learning, Five basic learning rules- Error correction Learning, Memory based learning.\n<\/p>\n

Module 2 <\/h4>\n

For complete syllabus and results, class timetable and more pls download iStudy<\/a>. Its a light weight, easy to use, no images, no pdfs platform to make students life easier.<\/p>\n

Module 3 <\/h4>\n

Supervised Learning- Decision Surfaces, Two-Category Separation, Linearly Separable Sets, Nonlinearly Separable Sets Unsupervised Learning- Clustering, Kohonen Networks and Competitive Learning, Hebbian Learning, Biomedical Applications, Diagnosis of CAD as a Clustering Problem, Other Biomedical Applications.\n<\/p>\n

Module 4 <\/h4>\n

Artificial Intelligence- Foundations of Computer-Assisted Decision Making Mathematical Modeling and Simulation, Pattern Recognition, Bayesian Analysis, Decision Theory, Symbolic Reasoning Techniques. Knowledge Representation- Production Rules- Introduction, Frames, Databases, Knowledge Acquisition- Introduction,Learned Knowledge, Meta-Knowledge.\n<\/p>\n

Module 5 <\/h4>\n

For complete syllabus and results, class timetable and more pls download iStudy<\/a>. Its a light weight, easy to use, no images, no pdfs platform to make students life easier.<\/p>\n

Course Outcomes:<\/h4>\n

After studying this course, students will be able to:<\/p>\n

    \n
  1. Describe the classes of neural networks and their models.<\/li>\n
  2. Explain the approaches to the development of neural network models.<\/li>\n
  3. Employ the learning techniques to classify the data.<\/li>\n
  4. Discuss general types of knowledge representations that are useful in decision-support systems.<\/li>\n
  5. Explain the reasoning methodologies utilized in knowledge based systems.<\/li>\n<\/ol>\n

    Question paper pattern:<\/h4>\n