Reinforcement Learning MCQs

1.     _________________ is a type of machine learning in which an agent learns to make decisions by interacting with an environment.

 

A. Supervised learning

B. Reinforcement learning

C. Semi-supervised learning

D. Unsupervised learning

Ans: B

 

2. Which of the following are applications of Reinforcement learning?

 

A. Robotics

B. video games

C. self-driving cars

D. All of the above

Ans: D

 3._________________ is defined as when an event, occurs due to a particular behavior, increases the strength and the frequency of the behavior.

 

A. Positive Reinforcement

B. Negative Reinforcement

C. Both A and B

D. None of the above

Ans : A

 

4. Which type of feedback does an agent in reinforcement learning receive?

A. Predictions
B. Labels
C. Clusters
D. Rewards or penalties

Ans: D

 5. ______________ is the Q-learning algorithm used for in reinforcement learning.

 

A. Image recognition

B. Clustering

C. Finding the optimal decision-making strategy

D. Natural language processing

Ans: C

 6. Which of the following are Advantages of reinforcement learning?

 

A. Maximizes Performance

B. Sustain Change for a long period of time

C. Too much Reinforcement can lead to an overload of states which can diminish the results

D. All of the above

Ans: D

 7.  In Reinforcement learning, the decision is made on the initial input or the input given at the start

  A. TRUE

B. FALSE

C. Can be true or false

D. Can not say

 Ans : B

 8. Which of the following is true about reinforcement learning?

 A. The agent gets rewards or penalty according to the action

B.    The agent navigates in an environmental building the example experiences or the training dataset.

C.    The target of an agent is to maximize the rewards and minimize the penalty.

D.    All of the above

Ans: D

 9. Q-Learning algorithm is off-policy algorithm because

A.    Policy being trained is exactly the one being executed

B.    Policy being trained is not necessarily the one being executed.
There is nothing called Q-Learning algorithm but Q-Value Iteration

D.    None of the above

Ans: B

 10. In Reinforcement learning decision is dependent, So we give labels to sequences of dependent decisions.

 

A. TRUE

B. FALSE

C. Can be true or false

D. Can not say

Ans: A

  WEEK 1 : b) Program to calculate the entropy and the information gain

 WEEK 1 : a) Write a program using scikit-learn to implement K-means Clustering

 WEEK 1 :  c) Program to implement perceptron

SVM

ARTIFICIAL INTELLIGENCE SYLLABUS

 ARTIFICIAL INTELLIGENCE

UNIT I

Introduction to Artificial Intelligence:

Introduction, History, Intelligent Systems, Foundations of AI, Applications, Tic- tac toe game playing, development of AI languages, Current trends in AI

Problem Solving:

State-Space Search and Control Strategies: Introduction, General Problem Solving, Characteristics of Problem, Exhaustive searches, heuristic search techniques, iterative deepening a*, Constraint Satisfaction

UNIT II

Problem Reduction and Game Playing: Introduction, problem reduction, game playing, alpha-beta pruning, two-player perfect information games Logic Concepts: Introduction, propositional calculus, proportional logic, natural deduction system, axiomatic system, semantic tableau system in proportional logic, resolution refutation in proportional logic, predicate logic

UNIT III

Knowledge Representation: Introduction, Approaches to Knowledge representation, Knowledge representation using Semantic Network, Extended Semantic Network for KR, Knowledge representation using Frames 

Advance Knowledge Representation Techniques: Introduction, Conceptual Dependency Theory, Script Structure, CYC Theory, Case Grammars, Semantic Web

UNIT IV

Expert Systems:

Expert Systems, Phases in building expert systems, Expert System Architecture, Rule Based Systems, Forward Chaining, Blackboard Systems, Blackboard architecture, Blackboard System vs Rulebased system, Truth maintenance system.

UNIT V

Uncertainty measure & Fuzzy sets and fuzzy logic:

Uncertainty measure: probability theory: Introduction, probability theory, Bayesian belief networks, certainty factor theory, dempster-shafer theory Fuzzy. 

sets and fuzzy logic: Introduction, fuzzy sets, fuzzy set operations, types of membership functions, multi valued logic, fuzzy logic, linguistic variables and hedges, fuzzy propositions, inference rules for fuzzy propositions, fuzzy systems.

Text Books:

1. Artificial Intelligence by Elaine Rich, Kevin Knight and Shivashankar B Nair, Tata McGraw Hill.

2. Introduction to Artificial Intelligence and Expert Systems by Dan W. Patterson, Pearson Education.

Reference Books:

1. Artificial Intelligence: A Modern Approach by S. Russell and P. Norvig, Prentice Hall

Software Engineering Syllabus

 Software Engineering

UNIT-I: Software and Software Engineering: The Nature of Software, The Unique Nature of WebApps, Software Engineering, Software Process, Software Engineering Practice, Software Myths. Process Models: A Generic Process Model, Process Assessment and Improvement, Prescriptive Process Models, Specialized Process Models, The Unified Process, Personal and Team Process Models, Process Terminology, Product and Process.

UNIT-II: Requirements Analysis And Specification: Requirements Gathering and Analysis, Software Requirement Specification (SRS), Formal System Specification. Software Design: Overview of the Design Process, How to Characterise of a Design?, Cohesion and Coupling, Layered Arrangement of Modules, Approaches to Software Design

UNIT – III: Function-Oriented Software Design: Overview of SA/SD Methodology, Structured Analysis, Developing the DFD Model of a System, Structured Design, Detailed Design, Design Review, over view of Object Oriented design. User Interface Design: Characteristics of Good User Interface, Basic Concepts, Types of User Interfaces, Fundamentals of Component-based GUI Development, A User Interface Design Methodology.

UNIT – IV: Coding And Testing: Coding, Code Review, Software Documentation, Testing, Unit Testing, Black-Box Testing, White-Box Testing, Debugging, Program Analysis Tool, Integration Testing, Testing Object-Oriented Programs, System Testing, Some General Issues Associated with Testing

UNIT – V: Software Reliability And Quality Management: Software Reliability, Statistical Testing, Software Quality, Software Quality Management System, ISO 9000, SEI Capability Maturity Model. Computer Aided Software Engineering: Case and its Scope, Case Environment, Case Support in Software Life Cycle, Other Characteristics of Case Tools, Towards Second Generation CASE Tool, Architecture of a Case Environment.

UNIT – VI Software Maintenance: Software maintenance, Maintenance Process Models, Maintenance Cost, Software Configuration Management. Software Reuse: what can be reused? Why almost No Reuse So Far? Basic Issues in Reuse Approach, Reuse at Organization Level.

TEXT BOOKS:

1.     Software engineering A practitioner’s Approach, Roger S. Pressman, Seventh Edition McGrawHill International Edition.

2.     Fundamentals of Software Engineering, Rajib Mall, Third Edition, PHI.

3.     Software Engineering, Ian Sommerville, Ninth edition, Pearson education

REFERENCE BOOKS:

1.     Software Engineering : A Primer, Waman S Jawadekar, Tata McGraw-Hill, 2008

2.     Software Engineering, A Precise Approach, Pankaj Jalote, Wiley India, 2010.

3.     Software Engineering, Principles and Practices, Deepak Jain, Oxford University Press.

4.     Software Engineering1: Abstraction and modeling, Diner Bjorner, Springer International edition, 2006