12/17/24, 8:26 AM about:blank

K L Deemed to be University
Department of Computer Science and Engineering-Honors -- KLBCH
Course Handout
2024-2025, Even Sem
Course Title :ADVANCED ALGORITHMS & DATA STRUCTURES
Course Code :23CS03HF
L-T-P-S Structure : 3-0-2-0
Pre-requisite :
Credits :4
Course Coordinator :Yamini Devi Jonnala
Team of Instructors :
Teaching Associates :
Syllabus :Introduction to Advanced Data Structures: Review of Basic Data Structures (arrays, linked lists,
stacks, queues), AVL trees: Properties, Insertion, Deletion, Red-Black trees: Properties, Rotation Operations,
B-Trees: Structure, Insertion, Deletion, Searching, Hash Tables: Collision Resolution Techniques (Chaining,
Open Addressing). Algorithm Design Techniques, Divide and Conquer: Principles, Merge Sort, Quicksort,
Closest Pair, Greedy Algorithms: Strategies, Minimum Spanning Tress, Shortest Paths, Huffman Coding,
Dynamic Programming: Principles, Matrix Chain Multiplication, Longest Common Subsequence, Knapsack
Problem. Graph Algorithms: Depth-First Search (DFS) and Breadth-First Search (BFS), Single Source
Shortest Path (Dijkstra’s Algorithm), All-Pairs Shortest Path (Floyd-Warshall Algorithm), Minimum
Spanning Trees (Prim’s and Kruskal’s Algorithm). Advanced Topics in Algorithms: Network Flow
Algorithms: Ford-Fulkerson Method, Edmonds-Karp Algorithm, Max-Flow Min-Cut Theorem, String
Matching Algorithms: Knuth-Morris-Pratt Algorithm, Rabin-Karp Algorithm, Boyer-Moore Algorithm,
Approximation Algorithms: Greedy Algorithms for NP-Hard Problems, Approximation Ratio, Analysis.
Text Books :Introduction to Algorithms, second edition, T.H.Cormen, C.E.Leiserson, R.L.Rivest, and
C.Stein, 3rd, 2009, The MIT Press
Reference Books :1. Algorithm Design Manual, Steven S.Skiena., 2nd , 2008, Springer. 2. Data Structures
and Algorithms in Python, Michael Goodrich, Roberto Tamassia and Michael H. Goldwasser., 2nd, 2013,
Wiley. 3.The Art of Computer Programming, Donald E.Knuth, 3rd, 1997, Addison-Wesley Professional.
Web Links :1. https://archive.nptel.ac.in/courses/106/102/106102064/# - Data Structures And Algorithms –
Prof. Naveen Garg, Department of Computer Science & Engineering, IIT Delhi, 2.
https://www.coursera.org/specializations/ boulder-data-structures-algorithms - Foundations of Data
Structures and Algorithms Specialization – University of Colarado Boulder.
MOOCS :Algorithms Specialization (Stanford University) :1.Graph Search, Shortest Paths, and Data
Structures : https://www.coursera.org/learn/algorithms-graphs-data-structures?specialization=algorithms , 2.
Divide &Conquer, Sorting and Searching, and Randomized Algorithms :
https://www.coursera.org/learn/algorithms-divide-conquer?specialization=algorithms, 3. Greedy
Algorithms, Minimum Spanning Trees, and Dynamic Programming :
https://www.coursera.org/learn/algorithms-greedy?specialization=algorithms.
Course Rationale :Student will explore several fundamental data structures and algorithms in computer
science and learns to implement them. Use of appropriate Data Structures enables a computer system to
perform its task more efficiently, by influencing the ability of computer to store and retrieve data. So, it
leads to skill-development.
Course Objectives :The objective of this course is to familiarize students with basic data structures and
their use in fundamental algorithms. It strengthens the ability to the students to identify and apply the
suitable data structure for the given real-world problems. It enables them to gain knowledge in practical
applications of data structures and implementation of basic data structures and algorithms for the
development of common practical applications. Analyze the asymptotic performance of algorithms and
apply important algorithmic design paradigms and methods of analysis.
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COURSE OUTCOMES (COs):

Blooms
CO Taxonomy
Course Outcome (CO) PO/PSO
NO Level
(BTL)
Demonstrate a comprehensive understanding of advanced data
CO1 PO1,PO3,PO6,PSO1 2
structures such as balanced trees, hash tables, and graphs.
Develop proficiency in designing, analyzing and optimizing
CO2 PSO1,PO2,PO3,PO6,PO1 2
complex algorithms.
Effectively apply graph algorithms to solve real-world
CO3 PO6,PO3,PO2,PO1,PSO1 3
problems.
Apply dynamic programming and backtracking techniques to
CO4 PSO1,PO1,PO6,PO3 3
solve combinatorial and optimization problems.
CO5 Analyze various advanced algorithms and data structures. PSO1,PO1,PO3,PO6 4

COURSE OUTCOME INDICATORS (COIs)::

Outcome Highest
COI-2 COI-3 COI-4
No. BTL
Btl-2
Summarizing the concepts
CO1 2
of Advanced Data
Structures.
Btl-2
Summarizing the algorithm
CO2 2
designing techniques and
their analysis.
Btl-3
Btl-2
Apply various Graph
CO3 3 Summarizing the concepts
Algorithms to solve the
of Graph Algorithms.
real-world problems.
Btl-2
Btl-3
Summarizing the algorithm
Apply the Algorithm Design
CO4 3 designing techniques
Strategies to solve the real-
Dynamic Programming and
world problems.
Backtracking.
Btl-4
The student will be able to
apply and analyze various
CO5 4
advanced algorithms and
data structures to solve the
real-world problems.

PROGRAM OUTCOMES & PROGRAM SPECIFIC OUTCOMES (POs/PSOs)

Po
Program Outcome
No.

Lecture Course DELIVERY Plan:

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Teaching-
Book No[CH
Sess.No. CO COI Topic Learning EvaluationComponents
No][Page No]
Methods

Course Handout
Explanation, Introduction TBOOK[1], CH
COI- End Semester
1 CO1 to Basic Data Structures 1.4.1-, 1.4.4, Chalk,PPT,Talk
2 Exam,SEM-EXAM1
Arrays, Linked Lists and P.No. 62-76.
their operations.

Review of Basic Data

TBOOK[1], CH
COI- Structures Stacks and End Semester
2 CO1 3.10.1-, 3.10.4, Chalk,PPT,Talk
2 Queues, Operations Exam,SEM-EXAM1
P.No. 200-214.
performed on them.

AVL trees: Properties, REF.BOOK[2],

COI- ALM,End Semester
3 CO1 Rotations performed on CH 11.3.1, Chalk,PPT,Talk
2 Exam,SEM-EXAM1
AVL Trees P.No. 481-487

AVL trees Operations : REF.BOOK[2],

COI- End Semester
4 CO1 Insertion, deletion and CH 11.3.2, Chalk,PPT,Talk
2 Exam,SEM-EXAM1
Searching P.No. 487-489.

Red-Black trees: TBOOK[1], CH End Semester

COI-
5 CO1 Properties, Rotation 3.13.1-, 3.13.4, Chalk,PPT,Talk Exam,MOOCs
2
Operations P.No. 273-288. Review,SEM-EXAM1

TBOOK[1], CH
B-Trees: Structure,
COI- 5.18.1, ALM,End Semester
6 CO1 Insertion, Deletion, Chalk,PPT,Talk
2 5.18.2,5.18.3, Exam,SEM-EXAM1
Searching
P.No. 221-245.

Hash Tables: Collision End Semester

TBOOK[1], CH
COI- Resolution Techniques Exam,Home
7 CO1 3.11.1-, 3.11.5, Chalk,PPT,Talk
2 (Chaining, Open Assignment,SEM-
P.No. 273-288.
Addressing). EXAM1

Asymptotic Notations,
TBOOK[1], CH
COI- Big Oh, Small Oh, End Semester
8 CO1 1.3.1-, 1.3.2, Chalk,PPT,Talk
2 Omega and Theta Exam,SEM-EXAM1
P.No. 41-51.
notations,

Performance Analysis of
Algorithms: Time TBOOK[1], CH
COI- End Semester
9 CO1 Complexity, Space 1.4.1-, 1.4.4, Chalk,PPT,Talk
2 Exam,SEM-EXAM1
Complexity, Master P.No. 62-76.
Theorem

Algorithm Design
Techniques, Introduction TBOOK[1], CH
COI- End Semester
10 CO2 to Divide and Conquer, 2.3.1, P.No. 28- Chalk,PPT,Talk
2 Exam,SEM-EXAM1
Approach, General 29.
Method, Binary Search.

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Teaching-
Book No[CH
Sess.No. CO COI Topic Learning EvaluationComponents
No][Page No]
Methods

Merge Sort, Description, TBOOK[1], CH

COI- End Semester
11 CO2 Example, Algorithm and 2.3.1-2.3.2, Chalk,PPT,Talk
2 Exam,SEM-EXAM1
its Analysis P.No. 29-36.

Quick Sort, Description, TBOOK[1], CH

COI- End Semester
12 CO2 Example, Algorithm and 2.7.1-2.7.4, Chalk,PPT,Talk
2 Exam,SEM-EXAM1
its Analysis P.No. 145-155.

Closest Pair, Description, TBOOK[1], CH

COI- End Semester
13 CO2 Example, Algorithm and 7.33.4, P.No. Chalk,PPT,Talk
2 Exam,SEM-EXAM1
its Analysis 957-967

Greedy Algorithms:
Strategies, Minimum
TBOOK[1], CH End Semester
COI- Spanning Tress,
14 CO2 4.16.1-4.16.2, Chalk,PPT,Talk Exam,MOOCs
2 Description, Example,
P.No. 370-379. Review,SEM-EXAM1
Algorithm and its
Analysis

Shortest Paths, Huffman

TBOOK[1], CH
COI- Coding, Description, ALM,End Semester
15 CO2 4.16.3, P.No. Chalk,PPT,Talk
2 Example, Algorithm and Exam,SEM-EXAM1
385-392
its Analysis

Dynamic Programming:
Principles, Matrix Chain End Semester
TBOOK[1], CH
COI- Multiplication, Exam,Home
16 CO2 4.15.1-4.15.2, Chalk,PPT,Talk
2 Description, Example, Assignment,SEM-
P.No. 323-339.
Algorithm and its EXAM1
Analysis

Longest Common
Subsequence, TBOOK[1], CH
COI- End Semester
17 CO2 Description, Example, 4.15.4, P.No. Chalk,PPT,Talk
2 Exam,SEM-EXAM1
Algorithm and its 350-355
Analysis

Knapsack Problem,
REF.BOOK[1],
COI- Description, Example, ALM,End Semester
18 CO2 CH 13.10, P.No. Chalk,PPT,Talk
2 Algorithm and its Exam,SEM-EXAM1
427-430
Analysis

Graph Algorithms:
Depth-First Search TBOOK[1], CH
COI- End Semester
19 CO3 (DFS), Description, 6.22.3, P.No. Chalk,PPT,Talk
3 Exam,SEM-EXAM2
Example, Algorithm and 540-548
its Analysis

20 CO3 COI- Breadth-First Search TBOOK[1], CH Chalk,PPT,Talk End Semester

3 (BFS), Description, 6.22.2, P.No. Exam,Home
531-539.
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Teaching-
Book No[CH
Sess.No. CO COI Topic Learning EvaluationComponents
No][Page No]
Methods
Example, Algorithm and Assignment,SEM-
its Analysis EXAM2

Single Source Shortest

TBOOK[1], CH
COI- Path (Dijkstra’s ALM,End Semester
21 CO3 6.24.3, P.No. Chalk,PPT,Talk
3 Algorithm) and its Exam,SEM-EXAM2
595-600.
Analysis

All-Pairs Shortest Path

TBOOK[1], CH
COI- (Floyd-Warshall End Semester
22 CO3 6.25.2, P.No. Chalk,PPT,Talk
3 Algorithm) and its Exam,SEM-EXAM2
629-635.
Anlaysis

Minimum Spanning TBOOK[1], CH

COI- ALM,End Semester
23 CO3 Trees (Prim’s Algorithm) 6.23.1-6.23.2, Chalk,PPT,Talk
3 Exam,SEM-EXAM2
and its Analysis P.No. 561-567.

Minimum Spanning
TBOOK[1], CH End Semester
COI- Trees (Kruskal’s
24 CO3 6.23.1-6.23.2, Chalk,PPT,Talk Exam,MOOCs
3 Algorithm) and its
P.No. 561-567. Review,SEM-EXAM2
Analysis

Advanced Topics in
Algorithms: Network
Flow Algorithms: Ford- TBOOK[1], CH
COI- End Semester
25 CO4 Fulkerson Method, 6.26.1, P.No. Chalk,PPT,Talk
3 Exam,SEM-EXAM2
Description, Example, 643-656.
Algorithm and its
Analysis

Edmonds-Karp
TBOOK[1], CH
COI- Algorithm, Description, End Semester
26 CO4 6.26.2, P.No. Chalk,PPT,Talk
3 Example, Algorithm and Exam,SEM-EXAM2
661-660.
its Analysis

Max-Flow Min-Cut
TBOOK[1], CH
COI- Theorem. Description, End Semester
27 CO4 6.26.2, P.No. Chalk,PPT,Talk
3 Example, Algorithm and Exam,SEM-EXAM2
657-663.
its Analysis

String Matching TBOOK[1], CH

COI- End Semester
28 CO4 Algorithms: Knuth- 7.32.4, P.No. Chalk,PPT,Talk
3 Exam,SEM-EXAM2
Morris-Pratt Algorithm 923-932.

Rabin-Karp Algorithm
TBOOK[1], CH
COI- ,Description, Example, ALM,End Semester
29 CO4 7.32.2, P.No. Chalk,PPT,Talk
3 Algorithm and its Exam,SEM-EXAM2
911-915.
Analysis

30 CO4 COI- Boyer-Moore Algorithm, REF.BOOK[2], Chalk,PPT,Talk End Semester

3 Description, Example, CH 13.2.2, Exam,Home
P.No. 586-589.
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Teaching-
Book No[CH
Sess.No. CO COI Topic Learning EvaluationComponents
No][Page No]
Methods
Algorithm and its Assignment,SEM-
Analysis EXAM2

Computational
TBOOK[1], CH
COI- Complexity : End Semester
31 CO4 7.34.1, P.No. Chalk,PPT,Talk
3 Deterministic and Non- Exam,SEM-EXAM2
966-978.
deterministic algorithms,

TBOOK[1], CH End Semester

COI- Class P and NP,
32 CO4 7.34.2, P.No. Chalk,PPT,Talk Exam,MOOCs
3 Examples
979-983. Review,SEM-EXAM2

NP - Hard and NP- TBOOK[1], CH

COI- ALM,End Semester
33 CO4 Complete classes, Cook’s 7.34.3-7.34.5, Chalk,PPT,Talk
3 Exam,SEM-EXAM2
theorem. P.No. 984-1021.

Approximation TBOOK[1], CH
COI- Algorithms : Greedy 7.35.1-7.35.2, End Semester
34 CO4 Chalk,PPT,Talk
3 Algorithms for NP-Hard P.No. 1022- Exam,SEM-EXAM2
Problems 1032.

TBOOK[1], CH
COI- Approximation Ratio, End Semester
35 CO4 7.35.3-7.35.5, Chalk,PPT,Talk
3 Analysis Exam,SEM-EXAM2
P.No. 984-1021.

Introduction to
REF.BOOK[1],
COI- Backtracking and its End Semester
36 CO4 CH 7.1, P.No. Chalk,PPT,Talk
3 Terminology, The Eight Exam,SEM-EXAM2
231-246
Queens Problem

REF.BOOK[1],
COI- Sum of the Subsets, End Semester
37 CO4 CH 7.1, P.No. Chalk,PPT,Talk
3 Graph Coloring Exam,SEM-EXAM2
231-246

Introduction to Branch &

TBOOK[1], CH
COI- Bound Technique, 0/1 End Semester
38 CO4 8.1, P.No. 379- Chalk,PPT,Talk
3 Knapsack using FIFO Exam,SEM-EXAM2
401.
and LCBB

Travelling Salesperson TBOOK[1], CH

COI- End Semester
39 CO4 Problem using Branch & 8.1, P.No. 401- Chalk,PPT,Talk
3 Exam,SEM-EXAM2
Bound Technique 405.

Lecture Session wise Teaching – Learning Plan

SESSION NUMBER : 1

No Session Outcomes are mapped

Time(min) Topic BTL Teaching- Active

Learning Learning
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Methods Methods
--- NOT
5 Attendance 1 Talk APPLICABLE
---
--- NOT
10 Course Handout Explanation. 2 PPT APPLICABLE
---
--- NOT
Arrays, Linked Lists and operations performed on them
20 2 PPT APPLICABLE
with examples.
---
--- NOT
Analysis of Algorithms related to problems using Arrays
10 2 PPT APPLICABLE
and Linked Lists.
---
--- NOT
5 Conclusion & Summary. 1 PPT APPLICABLE
---

SESSION NUMBER : 2

No Session Outcomes are mapped

Teaching- Active
Time(min) Topic BTL Learning Learning
Methods Methods
--- NOT
5 Attendance / Recap / Poll / Pop Question. 1 Talk APPLICABLE
---
--- NOT
10 Introduction to the basic concepts of Stacks and Queues 2 PPT APPLICABLE
---
--- NOT
Operations performed on Stacks and Queues with
20 2 PPT APPLICABLE
examples.
---
--- NOT
Analysis of Algorithms related to problems using Stacks
10 2 PPT APPLICABLE
and Queues.
---
--- NOT
5 Conclusion & Summary. 1 PPT APPLICABLE
---

SESSION NUMBER : 3

No Session Outcomes are mapped

Teaching- Active
Time(min) Topic BTL Learning Learning
Methods Methods
--- NOT
5 Attendance / Recap / Poll / Pop Question. 1 Talk APPLICABLE
---
One minute
30 Description of AVL trees, properties and Rotations. 2 PPT
paper
10 Examples of problem solving using AVL Trees. 1 PPT --- NOT
APPLICABLE
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---
--- NOT
5 Conclusion & Summary. 1 PPT APPLICABLE
---

SESSION NUMBER : 4

No Session Outcomes are mapped

Teaching- Active
Time(min) Topic BTL Learning Learning
Methods Methods
--- NOT
5 Attendance / Recap / Poll / Pop Question. 1 Talk APPLICABLE
---
--- NOT
10 Need of Rotations for Insertion and Deletion operations 2 PPT APPLICABLE
---
--- NOT
20 Operations performed on AVL Trees with examples. 2 PPT APPLICABLE
---
--- NOT
10 Applications of AVL Trees. 2 PPT APPLICABLE
---
--- NOT
5 Conclusion & Summary. 1 PPT APPLICABLE
---

SESSION NUMBER : 5

No Session Outcomes are mapped

Teaching- Active
Time(min) Topic BTL Learning Learning
Methods Methods
--- NOT
5 Attendance / Recap / Poll / Pop Question. 1 Talk APPLICABLE
---
--- NOT
30 Description of Red-Black Trees, properties and rotations. 2 PPT APPLICABLE
---
--- NOT
10 Examples of problem solving using Red-Black Trees. 2 PPT APPLICABLE
---
--- NOT
5 Conclusion & Summary 1 PPT APPLICABLE
---

SESSION NUMBER : 6

No Session Outcomes are mapped

Teaching- Active
Time(min) Topic BTL Learning Learning
Methods Methods
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--- NOT
5 Attendance / Recap / Poll / Pop Question 1 Talk APPLICABLE
---
One minute
30 B-Trees, properties and operations. 2 PPT
paper
--- NOT
10 Examples of problem solving using B-Trees. 2 PPT APPLICABLE
---
--- NOT
5 Conclusion & Summary. 1 PPT APPLICABLE
---

SESSION NUMBER : 7

No Session Outcomes are mapped

Teaching- Active
Time(min) Topic BTL Learning Learning
Methods Methods
--- NOT
5 Attendance / Recap / Poll / Pop Question. 1 Talk APPLICABLE
---
--- NOT
Hash Tables, Collision Resolution Techniques with
40 2 PPT APPLICABLE
examples
---
--- NOT
5 Conclusion & Summary 1 PPT APPLICABLE
---

SESSION NUMBER : 8

No Session Outcomes are mapped

Teaching- Active
Time(min) Topic BTL Learning Learning
Methods Methods
--- NOT
5 Attendance / Recap / Poll / Pop Question 1 Talk APPLICABLE
---
--- NOT
10 Description of the Asymptotic Notations. 2 PPT APPLICABLE
---
--- NOT
20 Description of the Asymptotic Notations. 2 PPT APPLICABLE
---
--- NOT
Examples of problem solving using Asymptotic
10 2 PPT APPLICABLE
Notations.
---
--- NOT
5 Conclusion & Summary. 1 Talk APPLICABLE
---

SESSION NUMBER : 9

No Session Outcomes are mapped

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Teaching- Active
Time(min) Topic BTL Learning Learning
Methods Methods
--- NOT
5 Attendance / Recap / Poll / Pop Question. 1 Talk APPLICABLE
---
--- NOT
Various techniques used to compute TC & SC of
10 2 PPT APPLICABLE
algorithms with examples.
---
--- NOT
Master Theorem for solving recurrence relations with
20 2 PPT APPLICABLE
examples.
---
--- NOT
Substitution Method for solving recurrence relations with
10 2 PPT APPLICABLE
examples.
---
--- NOT
5 Conclusion & Summary. 1 Talk APPLICABLE
---

SESSION NUMBER : 10

No Session Outcomes are mapped

Teaching- Active
Time(min) Topic BTL Learning Learning
Methods Methods
--- NOT
5 Attendance / Recap / Poll / Pop Question 1 Talk APPLICABLE
---
--- NOT
10 Description of the various Algorithm Design Strategies 2 PPT APPLICABLE
---
--- NOT
Divide and Conquer approach, General Method with
20 2 PPT APPLICABLE
Algorithm, Masters Theorem for D&C.
---
--- NOT
10 Binary Search and Finding Max. and Min. examples 2 PPT APPLICABLE
---
--- NOT
5 Conclusion & Summary. 1 Talk APPLICABLE
---

SESSION NUMBER : 11

No Session Outcomes are mapped

Teaching- Active
Time(min) Topic BTL Learning Learning
Methods Methods
--- NOT
5 Attendance / Recap / Poll / Pop Question. 1 Talk APPLICABLE
---
10 Description of Merge Sort with example. 2 PPT --- NOT
APPLICABLE
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---
--- NOT
Applying D&C approach to solve Merge Sort Problem
20 2 PPT APPLICABLE
with Algorithm.
---
--- NOT
Analyze the performance through computing the TC and
10 2 PPT APPLICABLE
SC of the Algorithm.
---
--- NOT
5 Conclusion & Summary. 1 PPT APPLICABLE
---

SESSION NUMBER : 12

No Session Outcomes are mapped

Teaching- Active
Time(min) Topic BTL Learning Learning
Methods Methods
--- NOT
5 Attendance / Recap / Poll / Pop Question. 1 Talk APPLICABLE
---
--- NOT
10 Description of Quick Sort with example. 2 PPT APPLICABLE
---
--- NOT
Applying D&C approach to solve Quick Sort Problem
20 2 PPT APPLICABLE
with Algorithm.
---
--- NOT
Analyze the performance through computing the TC and
10 2 PPT APPLICABLE
SC of the Algorithm.
---
--- NOT
5 Conclusion & Summary. 1 Talk APPLICABLE
---

SESSION NUMBER : 13

No Session Outcomes are mapped

Teaching- Active
Time(min) Topic BTL Learning Learning
Methods Methods
--- NOT
5 Attendance / Recap / Poll / Pop Question. 1 Talk APPLICABLE
---
--- NOT
Description of Closest Pair of Points problem with
10 2 PPT APPLICABLE
example.
---
--- NOT
Applying D&C approach to solve Closest Pair of Points
20 2 PPT APPLICABLE
problem with Algorithm.
---
--- NOT
Analyze the performance through computing the TC and
10 2 PPT APPLICABLE
SC of the Algorithm.
---

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--- NOT
5 Conclusion & Summary 1 Talk APPLICABLE
---

SESSION NUMBER : 14

No Session Outcomes are mapped

Teaching- Active
Time(min) Topic BTL Learning Learning
Methods Methods
--- NOT
5 Attendance / Recap / Poll / Pop Question. 1 Talk APPLICABLE
---
--- NOT
30 MST, Algorithm and Analysis 2 PPT APPLICABLE
---
--- NOT
10 Examples on Greedy, MST with Analysis 2 PPT APPLICABLE
---
--- NOT
5 Conclusion & Summary. 1 PPT APPLICABLE
---

SESSION NUMBER : 15

No Session Outcomes are mapped

Teaching- Active
Time(min) Topic BTL Learning Learning
Methods Methods
--- NOT
5 Attendance / Recap / Poll / Pop Question. 1 Talk APPLICABLE
---
Shortest Paths, Huffman Coding, Description, Example, Brain storming
40 2 PPT
Algorithm and its Analysis session
--- NOT
5 Conclusion & Summary 1 PPT APPLICABLE
---

SESSION NUMBER : 16

No Session Outcomes are mapped

Teaching- Active
Time(min) Topic BTL Learning Learning
Methods Methods
--- NOT
5 Attendance / Recap / Poll / Pop Question. 1 Talk APPLICABLE
---
--- NOT
Description of Dynamic Programming Approach,
10 2 PPT APPLICABLE
General Method with Algorithm.
---
20 Applying Greedy approach to Solve Matrix Chain 2 PPT --- NOT
Multiplication Problem with Algorithm. APPLICABLE
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---
--- NOT
Analyze the performance through computing the TC and
10 2 PPT APPLICABLE
SC of the Algorithm.
---
--- NOT
5 Conclusion & Summary 1 PPT APPLICABLE
---

SESSION NUMBER : 17

No Session Outcomes are mapped

Teaching- Active
Time(min) Topic BTL Learning Learning
Methods Methods
--- NOT
5 Attendance / Recap / Poll / Pop Question. 1 Talk APPLICABLE
---
--- NOT
Description of Longest Common Subsequence problem
10 2 PPT APPLICABLE
with example.
---
--- NOT
Applying DP approach to solve Longest Common
20 2 PPT APPLICABLE
Subsequence problem with Algorithm.
---
--- NOT
Analyze the performance through computing the TC and
10 2 PPT APPLICABLE
SC of the Algorithm.
---
--- NOT
5 Conclusion & Summary 1 PPT APPLICABLE
---

SESSION NUMBER : 18

No Session Outcomes are mapped

Teaching- Active
Time(min) Topic BTL Learning Learning
Methods Methods
--- NOT
5 Attendance / Recap / Poll / Pop Question. 1 Talk APPLICABLE
---
40 Description of Knapsack problem with example. 2 PPT Seminars
--- NOT
5 Conclusion & Summary 2 PPT APPLICABLE
---

SESSION NUMBER : 19

No Session Outcomes are mapped

Teaching- Active
Time(min) Topic BTL Learning Learning
Methods Methods
5 Attendance / Recap / Poll / Pop Question. 1 Talk --- NOT
APPLICABLE
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---
--- NOT
10 Description of Graph Algorithms and their need. 2 PPT APPLICABLE
---
--- NOT
20 DFS with Algorithm and example. 2 PPT APPLICABLE
---
--- NOT
Analyze the performance through computing the TC and
10 2 PPT APPLICABLE
SC of the Algorithm.
---
--- NOT
5 Conclusion & Summary 1 PPT APPLICABLE
---

SESSION NUMBER : 20

No Session Outcomes are mapped

Teaching- Active
Time(min) Topic BTL Learning Learning
Methods Methods
--- NOT
5 Attendance / Recap / Poll / Pop Question. 1 Talk APPLICABLE
---
--- NOT
10 Description of Breadth First Search and their need. 2 PPT APPLICABLE
---
--- NOT
20 BFS Algorithm and example. 3 PPT APPLICABLE
---
--- NOT
Analyze the performance through computing the TC and
10 3 PPT APPLICABLE
SC of the Algorithm.
---
--- NOT
5 Conclusion & Summary 1 PPT APPLICABLE
---

SESSION NUMBER : 21

No Session Outcomes are mapped

Teaching- Active
Time(min) Topic BTL Learning Learning
Methods Methods
--- NOT
5 Attendance / Recap / Poll / Pop Question. 1 Talk APPLICABLE
---
Single Source Shortest Path (Dijkstra’s Algorithm) and
30 3 PPT Seminars
its Analysis
--- NOT
Analyze the performance through computing the TC and
10 3 PPT APPLICABLE
SC of the Algorithm.
---
5 Conclusion & Summary. 1 PPT --- NOT
APPLICABLE
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---

SESSION NUMBER : 22

No Session Outcomes are mapped

Teaching- Active
Time(min) Topic BTL Learning Learning
Methods Methods
--- NOT
5 Attendance / Recap / Poll / Pop Question. 1 Talk APPLICABLE
---
--- NOT
Description of All-Pairs Shortest path problem and its
10 2 PPT APPLICABLE
need.
---
--- NOT
20 Floyd-Warshall Algorithm and example. 3 PPT APPLICABLE
---
--- NOT
Analyze the performance through computing the TC and
10 3 PPT APPLICABLE
SC of the Algorithm.
---
--- NOT
5 Conclusion & Summary 1 PPT APPLICABLE
---

SESSION NUMBER : 23

No Session Outcomes are mapped

Teaching- Active
Time(min) Topic BTL Learning Learning
Methods Methods
--- NOT
5 Attendance / Recap / Poll / Pop Question. 1 Talk APPLICABLE
---
Minimum Spanning Trees (Prim’s Algorithm) and its Brain storming
30 3 PPT
Analysis session
--- NOT
Analyze the performance through computing the TC and
10 3 PPT APPLICABLE
SC of the Algorithm.
---
--- NOT
5 Conclusion & Summary. 1 PPT APPLICABLE
---

SESSION NUMBER : 24

No Session Outcomes are mapped

Teaching- Active
Time(min) Topic BTL Learning Learning
Methods Methods
--- NOT
5 Attendance / Recap / Poll / Pop Question. 1 Talk APPLICABLE
---

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12/17/24, 8:26 AM about:blank

--- NOT
10 Description of Kruskal’s Algorithm and their need. 2 PPT APPLICABLE
---
--- NOT
20 Algorithm along with example. 3 PPT APPLICABLE
---
--- NOT
Analyze the performance through computing the TC and
10 3 PPT APPLICABLE
SC of the Algorithm.
---
--- NOT
5 Conclusion & Summary 1 PPT APPLICABLE
---

SESSION NUMBER : 25

No Session Outcomes are mapped

Teaching- Active
Time(min) Topic BTL Learning Learning
Methods Methods
--- NOT
5 Attendance / Recap / Poll / Pop Question. 1 Talk APPLICABLE
---
--- NOT
Description of Network Flow concepts and its
10 2 PPT APPLICABLE
applications.
---
--- NOT
Ford-Fulkerson Method description along with algorithm
20 3 PPT APPLICABLE
and example.
---
--- NOT
Analyze the performance through computing the TC and
10 3 PPT APPLICABLE
SC of the Algorithm.
---
--- NOT
5 Conclusion & Summary. 1 PPT APPLICABLE
---

SESSION NUMBER : 26

No Session Outcomes are mapped

Teaching- Active
Time(min) Topic BTL Learning Learning
Methods Methods
--- NOT
5 Attendance / Recap / Poll / Pop Question. 1 Talk APPLICABLE
---
--- NOT
10 Description of Edmonds-Karp Algorithm, its need. 2 PPT APPLICABLE
---
--- NOT
20 Algorithm along with example. 3 PPT APPLICABLE
---
10 Analyze the performance through computing the TC and 3 PPT --- NOT
SC of the Algorithm. APPLICABLE
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---
--- NOT
5 Conclusion & Summary. 1 PPT APPLICABLE
---

SESSION NUMBER : 27

No Session Outcomes are mapped

Teaching- Active
Time(min) Topic BTL Learning Learning
Methods Methods
--- NOT
5 Attendance / Recap / Poll / Pop Question. 1 Talk APPLICABLE
---
--- NOT
Description of Max-Flow Min-Cut concepts and its
10 2 PPT APPLICABLE
applications.
---
--- NOT
Max-Flow Min-Cut Theorem along with algorithm and
20 3 PPT APPLICABLE
example.
---
--- NOT
Analyze the performance through computing the TC and
10 3 PPT APPLICABLE
SC of the Algorithm.
---
--- NOT
5 Conclusion & Summary. 1 PPT APPLICABLE
---

SESSION NUMBER : 28

No Session Outcomes are mapped

Teaching- Active
Time(min) Topic BTL Learning Learning
Methods Methods
--- NOT
5 Attendance / Recap / Poll / Pop Question. 1 Talk APPLICABLE
---
--- NOT
10 Description of String-Matching Algorithms, its need. 2 PPT APPLICABLE
---
--- NOT
20 Knuth-Morris-Pratt Algorithm along with example. 3 PPT APPLICABLE
---
--- NOT
Analyze the performance through computing the TC and
10 3 PPT APPLICABLE
SC of the Algorithm.
---
--- NOT
5 Conclusion & Summary 1 PPT APPLICABLE
---

SESSION NUMBER : 29

No Session Outcomes are mapped

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12/17/24, 8:26 AM about:blank

Teaching- Active
Time(min) Topic BTL Learning Learning
Methods Methods
--- NOT
5 Attendance / Recap / Poll / Pop Question. 1 Talk APPLICABLE
---
One minute
30 Knuth-Morris-Pratt Algorithm along with example. 3 PPT
paper
--- NOT
Analyze the performance through computing the TC and
10 3 PPT APPLICABLE
SC of the Algorithm.
---
--- NOT
5 Conclusion & Summary. 1 PPT APPLICABLE
---

SESSION NUMBER : 30

No Session Outcomes are mapped

Teaching- Active
Time(min) Topic BTL Learning Learning
Methods Methods
--- NOT
5 Attendance / Recap / Poll / Pop Question. 1 Talk APPLICABLE
---
--- NOT
40 Boyer-Moore Algorithm along with example. 3 PPT APPLICABLE
---
--- NOT
5 Conclusion & Summary 1 PPT APPLICABLE
---

SESSION NUMBER : 31

No Session Outcomes are mapped

Teaching- Active
Time(min) Topic BTL Learning Learning
Methods Methods
--- NOT
5 Attendance / Recap / Poll / Pop Question. 1 Talk APPLICABLE
---
--- NOT
Description of basic concepts of Deterministic and Non-
10 2 PPT APPLICABLE
deterministic algorithms and its applications.
---
--- NOT
20 Deterministic algorithm examples and analysis. 3 PPT APPLICABLE
---
--- NOT
10 Non-Deterministic vs Deterministic Algorithms 3 PPT APPLICABLE
---
--- NOT
5 Conclusion & Summary. 1 PPT APPLICABLE
---
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SESSION NUMBER : 32

No Session Outcomes are mapped

Teaching- Active
Time(min) Topic BTL Learning Learning
Methods Methods
--- NOT
5 Attendance / Recap / Poll / Pop Question. 1 Talk APPLICABLE
---
--- NOT
40 Class P and NP, Examples 3 PPT APPLICABLE
---
--- NOT
5 Conclusion & Summary 1 PPT APPLICABLE
---

SESSION NUMBER : 33

No Session Outcomes are mapped

Teaching- Active
Time(min) Topic BTL Learning Learning
Methods Methods
--- NOT
5 Attendance / Recap / Poll / Pop Question. 1 Talk APPLICABLE
---
30 NP - Hard and NP-Complete classes with examples 3 PPT Case Study
--- NOT
10 Cooks Theorem 3 PPT APPLICABLE
---
--- NOT
5 Conclusion & Summary. 1 PPT APPLICABLE
---

SESSION NUMBER : 34

No Session Outcomes are mapped

Teaching- Active
Time(min) Topic BTL Learning Learning
Methods Methods
--- NOT
5 Attendance / Recap / Poll / Pop Question. 1 Talk APPLICABLE
---
--- NOT
10 Terminology related to Approximation Algorithms. 2 PPT APPLICABLE
---
--- NOT
Greedy Algorithms for NP-Hard Problems, Approximate
20 3 PPT APPLICABLE
Coloring Algorithm.
---
--- NOT
10 Planar Graph Coloring Problem examples. 3 PPT APPLICABLE
---

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12/17/24, 8:26 AM about:blank

--- NOT
5 Conclusion & Summary. 1 PPT APPLICABLE
---

SESSION NUMBER : 35

No Session Outcomes are mapped

Teaching- Active
Time(min) Topic BTL Learning Learning
Methods Methods
--- NOT
5 Attendance / Recap / Poll / Pop Question. 1 Talk APPLICABLE
---
--- NOT
10 Terminology related to Approximation Ratio. 2 PPT APPLICABLE
---
--- NOT
20 NP-Hard - Absolute Approximate Knapsack Problem. 2 PPT APPLICABLE
---
--- NOT
Max Clique Reduction to absolute approximation Max
10 3 PPT APPLICABLE
Clique.
---
--- NOT
5 Conclusion & Summary. 1 PPT APPLICABLE
---

SESSION NUMBER : 36

No Session Outcomes are mapped

Teaching- Active
Time(min) Topic BTL Learning Learning
Methods Methods
--- NOT
5 Attendance / Recap / Poll / Pop Question. 1 Talk APPLICABLE
---
--- NOT
10 Introduction to Backtracking. 2 PPT APPLICABLE
---
--- NOT
20 The Eight Queens Problem description 3 PPT APPLICABLE
---
--- NOT
Analyze the performance through computing the TC and
10 3 PPT APPLICABLE
SC of the Algorithm.
---
--- NOT
5 Conclusion & Summary. 1 PPT APPLICABLE
---

SESSION NUMBER : 37

No Session Outcomes are mapped

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Teaching- Active
Time(min) Topic BTL Learning Learning
Methods Methods
--- NOT
5 Attendance / Recap / Poll / Pop Question. 1 Talk APPLICABLE
---
--- NOT
10 Introduction to problem statements. 2 PPT APPLICABLE
---
--- NOT
20 Sum of the Subsets and its analysis. 3 PPT APPLICABLE
---
--- NOT
10 Graph Coloring and its analysis. 3 PPT APPLICABLE
---
--- NOT
5 Conclusion & Summary. 1 PPT APPLICABLE
---

SESSION NUMBER : 38

No Session Outcomes are mapped

Teaching- Active
Time(min) Topic BTL Learning Learning
Methods Methods
--- NOT
5 Attendance / Recap / Poll / Pop Question. 1 Talk APPLICABLE
---
--- NOT
10 Introduction to Branch & Bound Technique, terminology. 2 PPT APPLICABLE
---
--- NOT
20 0/1 Knapsack using FIFOBB and its analysis. 3 PPT APPLICABLE
---
--- NOT
10 0/1 Knapsack using LCBB and its analysis. 3 PPT APPLICABLE
---
--- NOT
5 Conclusion & Summary. 1 PPT APPLICABLE
---

SESSION NUMBER : 39

No Session Outcomes are mapped

Teaching- Active
Time(min) Topic BTL Learning Learning
Methods Methods
--- NOT
5 Attendance / Recap / Poll / Pop Question. 1 Talk APPLICABLE
---
10 Introduction to Branch & Bound Technique, terminology. 2 PPT --- NOT
APPLICABLE
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---
--- NOT
Travelling Salesperson Problem using Branch & Bound
20 3 PPT APPLICABLE
Technique.
---
--- NOT
Analyze the performance through computing the TC and
10 3 PPT APPLICABLE
SC of the Algorithm.
---
--- NOT
5 Conclusion & Summary. 1 PPT APPLICABLE
---

Tutorial Course DELIVERY Plan: NO Delivery Plan Exists

Tutorial Session wise Teaching – Learning Plan

No Session Plans Exists

Practical Course DELIVERY Plan:

Tutorial
Session Topics CO-Mapping
no

1 To implement basic Programs on Arrays and Linked Lists. CO5

2 To implement basic Programs on Stacks and Queues. CO5

3 To implement programs on AVL Trees and Red-Black Trees. CO5

4 To implement programs on B-Trees and Hash Tables. CO5

To implement programs on problem solving using Divide and Conquer –

5 CO5
Scenario1.

6 To implement programs on problem solving using Divide and Conquer CO5

To implement programs on problem solving using Greedy Approach –

7 CO5
Scenario1

To implement programs on problem solving using Greedy Approach –

8 CO5
Scenario2.

To implement programs on problem solving using Dynamic Programming

9 CO5
Approach – Scenario1.

To implement programs on problem solving using Dynamic Programming

10 CO5
Approach – Scenario2.

To implement programs on problem solving using Graph Algorithms–

11 CO5
Scenario1.

To implement programs on problem solving using Graph Algorithms–

12 CO5
Scenario2.

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Tutorial
Session Topics CO-Mapping
no

To implement programs on problem solving using Network Flow

13 CO5
Algorithms.

14 To implement programs on String Matching Algorithms – Scenario1. CO5

15 To implement programs on String Matching Algorithms – Scenario2. CO5

Practical Session wise Teaching – Learning Plan

SESSION NUMBER : 1

No Session Outcomes are mapped

Teaching- Active
Time(min) Topic BTL Learning Learning
Methods Methods
--- NOT
5 Attendance 1 Talk APPLICABLE
---
--- NOT
45 Basic Programs on Arrays and Linked Lists. 4 PPT APPLICABLE
---
--- NOT
50 Basic Programs on Arrays and Linked Lists. 4 PPT APPLICABLE
---

SESSION NUMBER : 2

No Session Outcomes are mapped

Teaching- Active
Time(min) Topic BTL Learning Learning
Methods Methods
--- NOT
5 Attendance 1 Talk APPLICABLE
---
--- NOT
45 Basic Programs on Stacks and Queues. 4 PPT APPLICABLE
---
--- NOT
50 Basic Programs on Stacks and Queues. 4 PPT APPLICABLE
---

SESSION NUMBER : 3

No Session Outcomes are mapped

Teaching- Active
Time(min) Topic BTL Learning Learning
Methods Methods
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--- NOT
5 Attendance 1 Talk APPLICABLE
---
--- NOT
45 Programs on AVL Trees and Red-Black Trees. 4 PPT APPLICABLE
---
--- NOT
50 Programs on AVL Trees and Red-Black Trees. 4 PPT APPLICABLE
---

SESSION NUMBER : 4

No Session Outcomes are mapped

Teaching- Active
Time(min) Topic BTL Learning Learning
Methods Methods
--- NOT
5 Attendance 1 Talk APPLICABLE
---
--- NOT
45 programs on B-Trees and Hash Tables. 4 PPT APPLICABLE
---
--- NOT
50 programs on B-Trees and Hash Tables. 4 PPT APPLICABLE
---

SESSION NUMBER : 5

No Session Outcomes are mapped

Teaching- Active
Time(min) Topic BTL Learning Learning
Methods Methods
--- NOT
5 Attendance 1 Talk APPLICABLE
---
--- NOT
45 programs on problem solving using Divide and Conquer 4 PPT APPLICABLE
---
--- NOT
50 programs on problem solving using Divide and Conquer 4 PPT APPLICABLE
---

SESSION NUMBER : 6

No Session Outcomes are mapped

Teaching- Active
Time(min) Topic BTL Learning Learning
Methods Methods
--- NOT
5 Attendance 1 Talk APPLICABLE
---

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--- NOT
45 programs on problem solving using Divide and Conquer 4 PPT APPLICABLE
---
--- NOT
50 programs on problem solving using Divide and Conquer 4 PPT APPLICABLE
---

SESSION NUMBER : 7

No Session Outcomes are mapped

Teaching- Active
Time(min) Topic BTL Learning Learning
Methods Methods
--- NOT
5 Attendance 1 Talk APPLICABLE
---
--- NOT
45 programs on problem solving using Greedy Approach 4 PPT APPLICABLE
---
--- NOT
50 programs on problem solving using Greedy Approach 4 PPT APPLICABLE
---

SESSION NUMBER : 8

No Session Outcomes are mapped

Teaching- Active
Time(min) Topic BTL Learning Learning
Methods Methods
--- NOT
5 Attendance 1 Talk APPLICABLE
---
--- NOT
45 programs on problem solving using Greedy Approach 4 PPT APPLICABLE
---
--- NOT
50 programs on problem solving using Greedy Approach 4 PPT APPLICABLE
---

SESSION NUMBER : 9

No Session Outcomes are mapped

Teaching- Active
Time(min) Topic BTL Learning Learning
Methods Methods
--- NOT
5 Attendance 1 Talk APPLICABLE
---
--- NOT
programs on problem solving using Dynamic
45 4 PPT APPLICABLE
Programming Approach
---

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--- NOT
programs on problem solving using Dynamic
50 4 PPT APPLICABLE
Programming Approach
---

SESSION NUMBER : 10

No Session Outcomes are mapped

Teaching- Active
Time(min) Topic BTL Learning Learning
Methods Methods
--- NOT
5 Attendance 1 Talk APPLICABLE
---
--- NOT
programs on problem solving using Dynamic
45 4 PPT APPLICABLE
Programming Approach
---
--- NOT
programs on problem solving using Dynamic
50 4 PPT APPLICABLE
Programming Approach
---

SESSION NUMBER : 11

No Session Outcomes are mapped

Teaching- Active
Time(min) Topic BTL Learning Learning
Methods Methods
--- NOT
5 Attendance 1 Talk APPLICABLE
---
--- NOT
45 programs on problem solving using Graph Algorithms 4 PPT APPLICABLE
---
--- NOT
50 programs on problem solving using Graph Algorithms 4 PPT APPLICABLE
---

SESSION NUMBER : 12

No Session Outcomes are mapped

Teaching- Active
Time(min) Topic BTL Learning Learning
Methods Methods
--- NOT
5 Attendance 1 Talk APPLICABLE
---
--- NOT
45 programs on problem solving using Graph Algorithms 4 PPT APPLICABLE
---
--- NOT
50 programs on problem solving using Graph Algorithms 4 PPT APPLICABLE
---

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12/17/24, 8:26 AM about:blank

SESSION NUMBER : 13

No Session Outcomes are mapped

Teaching- Active
Time(min) Topic BTL Learning Learning
Methods Methods
--- NOT
5 Attendance 1 Talk APPLICABLE
---
--- NOT
programs on problem solving using Network Flow
45 4 PPT APPLICABLE
Algorithms.
---
--- NOT
programs on problem solving using Network Flow
50 4 PPT APPLICABLE
Algorithms.
---

SESSION NUMBER : 14

No Session Outcomes are mapped

Teaching- Active
Time(min) Topic BTL Learning Learning
Methods Methods
--- NOT
5 Attendance 1 Talk APPLICABLE
---
--- NOT
45 programs on String Matching Algorithms 4 PPT APPLICABLE
---
--- NOT
50 programs on String Matching Algorithms 4 PPT APPLICABLE
---

SESSION NUMBER : 15

No Session Outcomes are mapped

Teaching- Active
Time(min) Topic BTL Learning Learning
Methods Methods
--- NOT
5 Attendance 1 Talk APPLICABLE
---
--- NOT
45 programs on String Matching Algorithms 4 PPT APPLICABLE
---
--- NOT
50 programs on String Matching Algorithms 4 PPT APPLICABLE
---

Skilling Course DELIVERY Plan: NO Delivery Plan Exists

Skilling Session wise Teaching – Learning Plan

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No Session Plans Exists

WEEKLY HOMEWORK ASSIGNMENTS/ PROBLEM SETS/OPEN ENDEDED PROBLEM-SOLVING EXERCISES etc:

Assignment Assignment
Week Topic Details co
Type No

COURSE TIME TABLE:

Hour 1 2 3 4 5 6 7 8 9
Day Component
Theory -- -- -- -- -- -- -- -- --
Tutorial -- -- -- -- -- -- -- -- --
Mon
Lab -- -- -- -- -- -- -- -- --
Skilling -- -- -- -- -- -- -- -- --
Theory H-S7 H-S7 --- --- --- --- --- -- --
Tutorial -- -- --- --- --- --- --- -- --
Tue
Lab -- -- --- --- --- --- --- H-S4,H-S4 H-S4,H-S4
Skilling -- -- --- --- --- --- --- -- --
Theory --- --- --- --- --- H-S7 --- --- ---
Tutorial --- --- --- --- --- -- --- --- ---
Wed
Lab --- --- --- --- --- -- --- --- ---
Skilling --- --- --- --- --- -- --- --- ---
Theory --- --- --- --- --- H-S4 H-S4 --- ---
Tutorial --- --- --- --- --- -- -- --- ---
Thu
Lab --- --- --- --- --- -- -- --- ---
Skilling --- --- --- --- --- -- -- --- ---
Theory --- --- --- --- --- --- --- -- --
Tutorial --- --- --- --- --- --- --- -- --
Fri
Lab --- --- --- --- --- --- --- H-S7,H-S7 H-S7,H-S7
Skilling --- --- --- --- --- --- --- -- --
Theory --- --- --- --- --- H-S4 --- --- ---
Tutorial --- --- --- --- --- -- --- --- ---
Sat
Lab --- --- --- --- --- -- --- --- ---
Skilling --- --- --- --- --- -- --- --- ---
Theory -- -- -- -- -- -- -- -- --
Tutorial -- -- -- -- -- -- -- -- --
Sun
Lab -- -- -- -- -- -- -- -- --
Skilling -- -- -- -- -- -- -- -- --

REMEDIAL CLASSES:

Supplement course handout, which may perhaps include special lectures and discussions that would be
planned, and schedule notified according

SELF-LEARNING:
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Assignments to promote self-learning, survey of contents from multiple sources.

S.no Topics CO ALM References/MOOCS

DELIVERY DETAILS OF CONTENT BEYOND SYLLABUS:

Content beyond syllabus covered (if any) should be delivered to all students that would be planned, and
schedule notified accordingly.
Advanced Topics, Additional Reading, Research
S.no CO ALM References/MOOCS
papers and any

EVALUATION PLAN:

Evaluation Evaluation Assessment Duration

Weightage/Marks CO1 CO2 CO3 CO4 CO5
Type Component Dates (Hours)
End Lab End Weightage 16 16
Semester 100
Semester Exam Max Marks 50 50
Summative
Evaluation End Semester Weightage 24 6 6 6 6
Total= 40 180
Exam
% Max Marks 100 25 25 25 25
MOOCs Weightage 6 1.5 1.5 1.5 1.5
120
Review Max Marks 100 25 25 25 25
Continuous Weightage 6 6
In Semester Evaluation - 120
Formative Lab Exercise Max Marks 50 50
Evaluation
Total= 24 Home Weightage 6 1.5 1.5 1.5 1.5
% Assignment and 120
Textbook Max Marks 100 25 25 25 25
Weightage 6 1.5 1.5 1.5 1.5
ALM 120
Max Marks 100 25 25 25 25
Lab In Weightage 8 8
100
In Semester Semester Exam Max Marks 50 50
Summative Weightage 14 7 7
Semester in
Evaluation 90
Exam-II Max Marks 50 25 25
Total= 36
% Semester in Weightage 14 7 7
90
Exam-I Max Marks 50 25 25

ATTENDANCE POLICY:

Every student is expected to be responsible for regularity of his/her attendance in class rooms and
laboratories, to appear in scheduled tests and examinations and fulfill all other tasks assigned to him/her in
every course
In every course, student has to maintain a minimum of 85% attendance to be eligible for appearing in
Semester end examination of the course, for cases of medical issues and other unavoidable circumstances the
students will be condoned if their attendance is between 75% to 85% in every course, subjected to
submission of medical certificates, medical case file and other needful documental proof to the concerned
departments

DETENTION POLICY :

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In any course, a student has to maintain a minimum of 85% attendance and In-Semester Examinations to be
eligible for appearing to the Semester End Examination, failing to fulfill these conditions will deem such
student to have been detained in that course.

PLAGIARISM POLICY :

Supplement course handout, which may perhaps include special lectures and discussions

COURSE TEAM MEMBERS, CHAMBER CONSULTATION HOURS AND CHAMBER VENUE DETAILS:

Supplement course handout, which may perhaps include special lectures and discussions
Chamber
Delivery Sections Chamber Chamber Signature of
Name of Consultation
Component of of Consultation Consultation Course
Faculty Timings for each
Faculty Faculty Day (s) Room No: faculty:
day
Yamini
L 4-MA - - - -
Jonnala
Yamini
P 4-A,7-B - - - -
Jonnala
Swathi D L 7-MA - - - -
Swathi D P 4-B,7-A - - - -

GENERAL INSTRUCTIONS

Students should come prepared for classes and carry the text book(s) or material(s) as prescribed by the
Course Faculty to the class.

NOTICES

Most of the notices are available on the LMS platform.

All notices will be communicated through the institution email.

All notices concerning the course will be displayed on the respective Notice Boards.

Signature of COURSE COORDINATOR

(Yamini Devi Jonnala)

Signature of Department Prof. Incharge Academics & Vetting Team Member

Department Of CSE-Honors

HEAD OF DEPARTMENT:

Approval from: DEAN-ACADEMICS

(Sign with Office Seal) [object HTMLDivElement]

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