Cognitive Computing

GANPAT UNIVERSITY

FACULTY OF ENGINEERING AND TECHNOLOGY

Programme

Bachelor of Technology

Branch/Spec.

Computer Science & Engineering (BDA)

Semester

VII

Version

1.0.0.0

Effective from Academic Year

2022-23

Effective for the batch Admitted in

June 2019

Subject code

2CSE712

Subject Name

COGNITIVE COMPUTING

Teaching scheme

Examination scheme (Marks)

(Per week)

Lecture(D)

Practical (Lab.)

Total

CE

SEE

Total

L

TU

P

TW

Credit

3

0

2

0

5

Theory

40

60

100

Hours

3

0

4

0

7

Practical

60

40

100

Pre-requisites:

Basics of Artificial Intelligence - Knowledge based Agent, Basics of Machine learning and Neural Network

Learning Outcome:

Upon Completion of the course, the students will be able to:

• Understand various concepts of cognitive computing
• Understand the evolution of Watson services from the original DeepQA architecture
• Describe various case studies related to cognitive computing.
• Develop cognitive computing related applications, like Chabot.

Theory syllabus

Unit

Content

Hrs

1

INTRODUCTION TO COGNITIVE SCIENCE AND COGNITIVE COMPUTING WITH AI:

Cognitive Computing, Cognitive Psychology, The Architecture of the Mind, The Nature of Cognitive Psychology, Cognitive architecture, Cognitive processes, The Cognitive Modeling Paradigms, Declarative / Logic based Computational cognitive modeling, connectionist models –Bayesian models. Introduction to Knowledge-Based AI – Human Cognition on AI – Cognitive Architectures

12

2

COGNITIVE COMPUTING WITH INFERENCE AND DECISION SUPPORT SYSTEMS:

Intelligent Decision making, Fuzzy Cognitive Maps, learning algorithms: Nonlinear Hebbian Learning, Data driven NHL, Hybrid learning, Fuzzy Grey cognitive maps, Dynamic Random fuzzy cognitive Maps

11

3

COGNITIVE COMPUTING WITH MACHINE LEARNING:

Machine learning Techniques for cognitive decision making, Hypothesis Generation and Scoring, Natural Language Processing, Representing Knowledge, Taxonomies and Ontologies, N-Gram models, Application

11

4

CASE STUDIES:

Cognitive Systems in health care, Cognitive Assistant for visually impaired – AI for cancer detection, Predictive Analytics, Text Analytics, Image Analytics, Speech Analytics – IBM Watson –

Introduction to IBM’s Power AI Platform - Introduction to Google’s TensorFlow Development Environment

11

Self learning: CASE studies

Practical content

Practicals will be based on natural language processing pipeline, Visual recognition pipeline and various Watson assistant services - text to speech, speech to text, language translator, chatbot, knowledge discovery.

Text Books

1

Hurwitz, Kaufman, and Bowles, “Cognitive Computing and Big Data Analytics”, Wiley, Indianapolis.

Reference Books

1

Jerome R. Busemeyer, Peter D. Bruza, “Quantum Models of Cognition and Decision”, Cambridge University Press.

2

Emmanuel M. Pothos, Andy J. Wills, “Formal Approaches in Categorization”, Cambridge University Press.

3

Nils J. Nilsson, “The Quest for Artificial Intelligence”, Cambridge University Press.

4

Neil Stillings, Steven E. Weisler, Christopher H. Chase and Mark H. Feinstein, “Cognitive Science: An Introduction”, MITPress.

Course Outcome

Cos

Description

CO1

Understand various concepts of cognitive computing

CO2

Understand the evolution of Watson services from the original DeepQA architecture

CO3

Describe various case studies related to cognitive computing.

CO4

Develop cognitive computing related applications, like Chabot.

Mapping of CO and PO: