Neural Network

 

Name of the faculty : Pratima Sharma
Discipline : CSE
Semester : VIIth sem
Subject : Neural Network
Lesson plan duration : From AUG. 2018 to NOV. 2018
Work load lecture per week (in hours) : 4 Lectures
Week	Theory		Practical
	Lecture Day	Topic(including assignement/ test)	Practical Day	Topic
1	1	Section A:Overview of biological neurons	1	Introduction to java
	2	Structure of biological neurons relevant to ANNs		Introduction to Matlab
	3	Models of ANNs
	4	Feedforward & feedback network
2	5	Revision till the covered topic	2	WAP to perform Airthematic Operations
	6	Learning rules; Hebbian learning rule
	7	Perception learning rule
	8	Delta learning rule
3	9	Widrow-Hoff learning rule	3	WAP of Branching statements
	10	Correction learning rule
	11	Winner –lake all elarning rule
	12	Revision of section A
4	13	Section B :Single layer Perception Classifier	4	WAP using Loops:For loop
	14	Classification model		WAP using Loops: while loop, do-while loop
	15	Features & Decision regions
	16	Training &classification using discrete perceptron
5	17	Single layer continuous perceptron networks for	5	Program to display a vector
	18	linearlyseperable classifications
	19	Multi-layer Feed forward Networks
	20	Linearly non-seperable pattern classification
6	21	Delta learning rule for multi-perceptron layer	6	Program to display a Matrix
	22	Generalized delta learning rule
	23	Error back-propagation training
	24	Learning factors
7	25	Revision	7	Program to Addition of a Matrix.
	26	Section C:Single layer feed back Networks
	27	Basic Concepts
	28	Hopfield networks
8	29	Hopfield networks	8	Program to transpose of a Matrix.
	30	Training & Examples
	31	Training & Examples
	32	Associative memories
9	33	Linear Association	9	Program on strings
	34	Basic Concepts of recurrent Auto associative memory
	35	Revision
	36	Retrieval algorithm
10	37	storage algorithm	10	Program of plotting functions
	38	By directional associative memory
	39	Association encoding & decoding
	40	Stability
11	41	Revision	11	Program of Arrays
	42	Section D:Self organizing networks
	43	UN supervised learning of clusters
	44	Winner-take-all learning
12	45	recall mode	12	Program of Arrays for finding largest number
	46	Initialisation of weights
	47	seperability limitations
	48	Revision
13	49	Revision	13	Program on application of Matlab
	50	Revision