Course Outcome (CO) Bloom’s Knowledge Level (KL)
At the end of course, the student will be able to understand
CO 1 Define the meaning of intelligence and study various intelligent agents. K1
CO 2 Understand, analyze and apply AI searching algorithms in different problem
domains.
K2 , K3, K4
CO 3 Study and analyze various models for knowledge representation. K1, K3
CO 4 Understand the basic concepts of machine learning to analyze and implement
widely used learning methods and algorithms.
K2 , K4, K6

Students are expected to analyze the problem statement/ case study and design a solution applying software engineering principles.

1. Write a program to draw line by using DDA algorithm.
2. Write a program to draw line by using Bresenham's algorithm.
3. Write a program to draw a circle using midpoint circle algorithm.
4. Write a program of Cohen-Sutherland algorithm.
5. Write a program to shear a cuboid.
6. Write a program to draw a polygon and perform rotation operation.
7. Write a program to draw a polygon and perform translation operation.
8. Write a program to draw a polygon and perform scaling operation.
9. Write a program to draw a rectangle by using Bresenham's algorithm.

UNIT-I
Android Architecture: Introduction to Android, Layouts, Views and Resources, Activities and Intents ,
Activity Lifecycle and Saving State, Activities and Implicit Intents, Testing & Debugging App ,
Android Support Libraries
UNIT-II
User Interaction and Intuitive Navigation: Input Controls, Menus, Widgets, Screen Navigation,
Recycler View, ListView, Adapters and Data Binding, Drawables, Themes and Styles
UNIT-III

UNIT-I
Introduction: Basics of pattern recognition, Design principles of pattern recognition system, Learning and
adaptation, Pattern recognition approaches, Mathematical foundations – Linear algebra, Probability Theory,
Expectation, mean and covariance, Normal distribution, Multivariate normal densities, Chi square test.
UNIT-II
Statistical Patten Recognition: Bayesian Decision Theory, Classifiers, Normal density and discriminant
functions.
UNIT-III

UNIT-I
Overview and Introduction to Machine Learning: Data Science, AI & ML , Introduction of Machine
intelligence and its applications, Machine learning concepts, Components of a learning problem, supervised,
unsupervised and reinforcement learning, inductive learning, deductive learning.
UNIT-II
Foundations of Machine Learning: Hypothesis Space and Inductive Bias, feature selection. Classification,
regression linear and polynomial, logistic regression, decision tree, random forest, naïve bayes, SVM.

UNIT-I
System definition and components, stochastic activities, continuous and discrete systems, System modeling.
Types of models, static and dynamic physical models, static and dynamic mathematical models, full corporate
model, types of system study.
UNIT-II
System simulation, Need of simulation, Basic nature of simulation, techniques of simulation, comparison of
simulation and analytical methods, types of system Simulation, real time simulation, hybrid simulation,

UNIT-I
Transaction and schedules, Concurrent Execution of transaction, Conflict and View Serializability, Testing for
Serializability, Concepts in Recoverable and Cascade less schedules.
UNIT–II
Lock based protocols, time stamp-based protocols, Multiple Granularity and Multi version Techniques, enforcing
serializability by Locks, Locking system with multiple lock modes, architecture for Locking scheduler
UNIT-III

UNIT-I
Query Processing and Optimization: Valuation of Relational Operations, Transformation of
Relational Expressions, Indexing and Query Optimization, Limitations of Relational Data Model, Null
Values and Partial Information.
UNIT-II
Objected Oriented and Object Relational Databases : Modeling Complex Data Semantics,
Specialization, Generalization, Aggregation and Association, Objects, Object Identity, Equality and
Object Reference, Architecture of Object Oriented and Object Relational Databases

UNIT-I
Distributed Systems: Introduction, Characteristics, Examples of distributed Systems, Resource sharing and Web
Challenges. Architectural models, Fundamental Models.
Theoretical Foundation for Distributed Systems: Limitation of Distributed system, absence of global clock,
shared memory, Logical clocks, Lamport’s & vectors logical clocks.
Concepts in Message Passing Systems: Causal order, total order, total causal order, Techniques for Message
Ordering, Causal ordering of messages, global state, and termination detection.