Neural Networks (CSC383) — Notes, Syllabus & Past Questions

Computational models based on biological neural systems for pattern recognition.

Elective Course8 Chapters
Chapter 1Past Questions

Introduction to Neural Network

Basics of neural networks and human brain, Models of a neuron, Neural Network viewed as Directed Graphs, Feedback, Network Architectures, Knowledge Representation, Learning Processes, Learning Tasks.

View Questions
Chapter 2Past Questions

Rosenblatt’s Perceptron

Introduction, Perceptron, The Perceptron Convergence Theorem, Relation between the Perceptron and Bayes Classifier for a Gaussian Environment, The Batch Perceptron Algorithm.

View Questions
Chapter 3Past Questions

Model Building through Regression

Introduction, Linear Regression Model: Preliminary Considerations, Maximum a Posteriori Estimation of the Parameter Vector, Relationship Between Regularized Least-Squares Estimation and Map Estimation.

View Questions
Chapter 4Past Questions

The Least-Mean-Square Algorithm

Introduction, Filtering Structure of the LMS Algorithm, Unconstrained Optimization: A Review, The Wiener Filter, The Least-Mean-Square Algorithm.

View Questions
Chapter 5Past Questions

Multilayer Perceptron

Introduction, Batch Learning and On-Line Learning, The Back-Propagation Algorithm, XOR problem, Heuristics for Making the back-propagation Algorithm Perform Better.

View Questions
Chapter 6Past Questions

Kernel Methods and Radial-Basis Function Networks

Introduction, Cover's Theorem on the separability of Patterns, The Interpolation problem, Radial-Basis-Function Networks, K-Means Clustering, Recursive Least-Squares Estimation of the Weight Vector.

View Questions
Chapter 7Past Questions

Self-Organizing Maps

Introduction, Two Basic Feature-Mapping Models, Self-Organizing Map, Properties of the Feature Map, Contextual Maps, Hierarchical Vector Quantization, Kernel Self-Organizing Map.

View Questions
Chapter 8Past Questions

Dynamic Driven Recurrent Networks

Introduction, Recurrent Network Architectures, Universal Approximation Theorem, Controllability and Observability, Computational Power of Recurrent Networks, Learning Algorithms.

View Questions

Related Study Resources for Neural Networks (CSC383)

Subject HubNeural Networks (CSC383)Official CurriculumNeural Networks SyllabusTU Board PapersNeural Networks Past QuestionsSemester Directory6th Sem Subjects