G.H.RAISONI COLLEGE OF ENGINEERING
DAY WISE TEACHING PLAN
M.Tech (CSE), Sem II
Sub: Advances in Algorithm (AoA)
Faculty Name : Ms. Urmila Shrawankar
Lect. No.
Topics
1
Algorithm paradigms
2
Asympotic notation
3
Divide and Conqure
4
Recurrences
5
Probabilistic Analysis
6
Randomized Algorithm
7
Dynamic Programming
8
Dynamic Programming
9
Longest Common subsequences
10
Greedy Strategy
11
Huffman codes
12
Aggregate Analysis
13
Accounting Method
14
Dynamic Tables
15
Bellman Ford Algorithm
16
Acyclic Graphs
17
Dijkstra's Algorithm
18
Shortest Paths Properties
19
Linear Equation
20
Inverting Matrix
21
Standard and Slack forms
22
Linear Programming
23
Simplex Algorithm
24
Polynomials
25
Number-Theoretic Algorithm
26
Modular Arithmetic
27
Chinese remainder theorem
28
String Matching Algorithm
29
Knuth-Morris -Pratt Algorithm
30
Polynomial time
31
Reducibility
32
NP-completeness
33
Approximation Algorithm
Ms. Urmila Shrawankar
Subject Teacher
Teaching Plan
Subject Name: Data Mining & Warehousing
Year/Sem:
2nd Sem MTech(CSE)
Faculty Name: S. S. Dongre
Lecture No
Unit No.
Topic/Topic Description
Lecture 1
Syllabus & Teaching Plan Discussion
Lecture 2
Mining & Data Warehousing : Introduction to
data mining
Lecture 3
data Warehousing
I
Lecture 4
Introduction to KDD process
Lecture 5
Classifications and algorithms
Lecture 6
Data mining tasks, Machine Learning- BasicConcept
Lecture 7
Data Warehouse Architecture , Data modeling.
Lecture 8
Course Review
Lecture 9
Data marts & olap: Data Mart Designing
Lecture 10
data mart builder, Data Mart Discovery
Lecture 11
On-line analytical processing, OLTP vs. DW
Environment
Lecture 12
Relationship of data mining and data
warehousing : Application of Data Mining
Lecture 13
Application of Data Ware housing
Lecture 14
A relation between Data Mining and Data
Warehousing according to need of business
Course Review
II
Lecture 15
Lecture 16
Statistical analysis and cluster analysis: What is
statistics ?
Difference between statistics and data mining
Lecture 17
Lecture 18
III
Histograms, Statistic for predictions
Lecture 19
clustering for clarity
Lecture 20
Hierarchical and Non-Hierarchical clusters
Lecture 21
Choosing classics
Lecture 22
Course Review
Lecture 23
Neural networks & mining complex: What are
neural Networks?
Lecture 24
IV
Where to use these Networks?
Lecture 25
Benefits and features of Networks
Lecture 26
Rule Induction, various mining complexities
Lecture 27
Course Review
Lecture 28
Next generation of informatics mining &
knowledge discovery : Business Intelligence
and Information Mining
Lecture 29
Text mining, Knowledge Management
Lecture 30
Benefits and Products of Text Mining
Lecture 31
Lecture 32
Customer Relationship Management in the eBusiness World
Recent trends in data mining.
Lecture 33
Course Review
Lecture 34
Lecture 35
V
University Question Paper discussion
Latest applications in Data Mining &
Warehouse
S. S. Dongre
Dr. L.G.Malik
[Subject Teacher]
[H.O.D.CSE]
TEACHING PLAN
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H. O. D.
Portation of Syllabus to be Covered
Introduction to pattern recognition
Statistical Pattern Recognition
Learning paradigm
Structure of pattern recognition system
Parametric pattern recognition
Statistical approach for pattern recognition
Bays classification
Classification error
Density estimation
Regression Analysis
Discriminant analysis
Empirical error criteria, MLE
Optimisation methods
Linear and quadratic discriminant
Shrinkage, Logistic Classification
Perceptrons & Maximum margin
Error correcting codes
Error assessment
Confidence intervals, Resampling methods
Comparing classifiers
Nonparametric classification: Histogram
Nearest neighbor method
Kernel approaches, Local polynomial fitting
Automatic kernel methods
Feature extraction: Optimal features
Linear transformations
Principal component analysis
Non linear principal component analysis
Feature subset selection
Recent trends in pattern recognition
Signature of Teacher