JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD

Prerequisites

Probability theory

Course Objectives:

• 1. To provide an understanding of the central challenges in realizing aspects of human cognition.
• 2. To provide a basic exposition to the goals and methods of human cognition.
• 3. To develop algorithms that use AI and machine learning along with human interaction and feedback to help humans make choices/decisions.
• 4. To support human reasoning by evaluating data in context and presenting relevant findings along with the evidence that justifies the answers.

Course Outcomes:

• 1. Understand the basics of cognitive computing and comparisons with traditional approaches.
• 2. Plan and use the primary tools associated with cognitive computing.
• 3. Plan and execute a project that leverages cognitive computing.
• 4. Understand and develop the business implications of cognitive computing.

UNIT - I

Introduction to Cognitive Science: Understanding Cognition, Design for Human Cognition, Augmented Intelligence, Cognition Modeling Paradigms: Declarative/ logic-based computational cognitive modeling, connectionist models of cognition, Bayesian models of cognition, a dynamical systems approach to cognition

UNIT - II

Cognitive Models of memory and language, computational models of episodic and semantic memory, modeling psycholinguistics

UNIT - III

Cognitive Modeling: modeling the interaction of language, memory and learning, Modeling select aspects of cognition classical models of rationality, symbolic reasoning and decision making

UNIT - IV

Formal models of inductive generalization, causality, categorization and similarity, the role of analogy in problem solving, Cognitive Development Child concept acquisition. Cognition and Artificial cognitive architectures such as ACT-R, SOAR, OpenCog, CopyCat, Memory Networks

UNIT - V

DeepQA Architecture, Unstructured Information Management Architecture (UIMA), Structured Knowledge, Business Implications, Building Cognitive Applications, Application of Cognitive Computing and Systems

Case study: IBM Watson

TEXT BOOKS:

• 1. The Cambridge Handbook of Computational Psychology by Ron Sun (ed.), Cambridge University Press.
• 2. Learning IBM Watson Analytics, James D Miller, Packt Publications

REFERENCES:

• 1. Formal Approaches in Categorization by Emmanuel M. Pothos, Andy J. Wills, Cambridge University Press.
• 2. Cognition, Brain and Consciousness: Introduction to Cognitive Neuroscience by Bernard J. Bears, Nicole M. Gage, Academic Press.
• 3. Cognitive Computing and Big Data Analytics by Hurwitz, Kaufman, and Bowles, Wiley