AIDS 8th Semester Syllabus

Unit 1Introduction to Big Data

Introduction to Big data · Big data characteristics · Types of big data · Traditional versus Big data · Evolution of Big data · Challenges with Big Data · Technologies available for Big Data · Infrastructure for Big data · Use of Data Analytics · Desired properties of Big Data system

Unit 2Introduction to Hadoop

Core Hadoop components · Hadoop Eco system · Hive Physical Architecture · Hadoop limitations · RDBMS Versus Hadoop · Hadoop Distributed File system · Processing Data with Hadoop · Managing Resources and Application with Hadoop YARN · MapReduce programming

Unit 3Hive and Pig

Hive Architecture · Hive Data types · Hive Query Language · Introduction to Pig · Anatomy of Pig · Pig on Hadoop · Use Case for Pig · ETL Processing · Data types in Pig · Running Pig · Execution model of Pig · Operators · Functions · Data types of Pig

Unit 4Introduction to NoSQL

NoSQL Business Drivers · NoSQL Data architectural patterns · Variations of NoSQL architectural patterns using NoSQL to Manage Big Data · Introduction to MongoDB

Unit 5Mining Social Network Graphs

Applications of social Network mining · Social Networks as a Graph · Types of social Networks · Clustering of social Graphs · Direct Discovery of communities in a social graph · Introduction to recommender system

Unit 1Introduction

Natural Language Processing (NLP): Definition and scope · Applications in various domains · Challenges and limitations · NLP tasks in syntax, semantics, and pragmatics · Different Data Models such as Boolean Model, Vector model, Probabilistic Model · Comparison of classical NLP models: Rule‑based model, Statistical model, Information retrieval model, Rule‑based machine translation model, Probabilistic Graphical model

Unit 2Linguistics and Morphology

Linguistic essentials: Phonetics and Phonology · Morphology: Morphens, Syntax, Semantics, Pragmatics, Semiotics, Discourse Analysis, Psycholinguistics, Corpus Linguistics · Word Formation Processes · Morphological analysis · Morphological Finite State Transducers

Unit 3Word Level Analysis

Tokenization · Part‑of‑Speech Tagging (POS Tagging) · Lemmatization · Stemming · Named Entity Recognition (NER) · Word Sense Disambiguation (WSD) · Word Embedding · Types of POS Tagging: Rule‑based, Stochastic, Transformation‑based, Lexical · Hidden Markov Model and Maximum Entropy model · n‑grams · Collocations · Applications of NER

Unit 4Syntax Analysis

Grammatical Formalisms: Context Free Grammars (CFG) · Grammar rules for English · Syntactic parsing: Grammar formalisms and treebanks · Efficient parsing for context‑free grammars (CFGs) · Statistical parsing and probabilistic CFGs (PCFGs)

Unit 5Semantic Analysis

Requirements for representation · First‑Order Logic · Description Logics · Syntax‑Driven Semantic analysis · Semantic attachments · Word senses, relations between senses, thematic roles, selectional restrictions · Word‑sense disambiguation (WSD) using supervised, dictionary and thesaurus methods · Applications of WSD

Unit 1Introduction to Reinforcement Learning

Definition of reinforcement learning framework and Markov Decision Process ($MDP$) · Policies · Value functions and Bellman equations · Exploration vs. exploitation strategies · Components and architecture of an RL agent · Common challenges and problems in reinforcement learning

Unit 2Bandit Algorithms and Dynamic Programming

Multi‑armed bandit algorithms: Upper Confidence Bound (UCB), Probably Approximately Correct (PAC), Median Elimination · Policy gradient methods for bandits · Full reinforcement learning and $MDP$ formulation · Bellman optimality principle · Dynamic programming techniques: value iteration, policy iteration, and policy evaluation · Extensions of DP and convergence analysis using contraction mapping · Monte‑Carlo (MC) learning · Temporal‑Difference (TD) learning · TD‑$\lambda$ and eligibility traces

Unit 3Q‑Learning, Function Approximation and Deep Q‑Networks

Q‑learning algorithm and its relation to temporal‑difference methods · Eligibility traces in Q‑learning · Function approximation for value functions: linear, non‑linear, and least‑squares methods · Incremental vs. batch update methods · Deep Q‑Learning analysis and stability considerations · Deep Q‑Network (DQN) architecture · Experience replay mechanisms

Unit 4Advanced Q‑Learning, Policy Gradient, Hierarchical and POMDPs

Fitted Q‑iteration and its applications · Advanced Q‑learning variants and handling deep Q‑learning problems · Imitation learning from optimal controllers · Integration of DQN with policy‑gradient approaches · Policy‑gradient algorithms for full reinforcement learning · Hierarchical reinforcement learning concepts · Partially observable Markov decision processes (POMDPs)

Unit 5Actor‑Critic, Inverse RL and Current Trends

Actor‑Critic methods and their variants · Inverse reinforcement learning (IRL) · Maximum entropy deep inverse reinforcement learning · Generative adversarial imitation learning (GAIL) · Recent trends in reinforcement learning architectures

Unit 1Introduction to RPA

Scope and techniques of automation · Robotic process automation overview · Benefits of RPA · Components of RPA · RPA platforms · Future of automation · History of automation · Comparison of RPA vs Automation · Processes and flowcharts · Programming constructs in RPA · Types of bots · RPA development methodologies · Robotic control‑flow architecture · RPA business case · Industries best suited for RPA · Risks and challenges with RPA · Emerging RPA ecosystem

Unit 2RPA Tool Introduction and Basics

Introduction to RPA tool · User interface overview · Variables in RPA · Managing variables · Managing arguments · Arguments panel · Imported namespaces · Control flow: If‑Else statements · Loops in RPA · Advanced control flow constructs · Data manipulation: scalar variables · Data manipulation: collections and tables · Text manipulation · Data gathering and assembling

Unit 3Advanced Automation Concepts & Techniques

Basic and desktop recording · Web recording · Input‑output methods · Screen scraping · Data scraping techniques · Selectors: definition and assessment · Selector customization · Debugging selectors · Dynamic selectors · Partial selectors · Image, text and advanced Citrix automation challenges · Excel data tables and PDF handling · Data tables in RPA · Excel and data table basics · Data manipulation in Excel · Extracting data from PDF · Extracting single data items · Anchors in PDF · Using anchors in PDF

Unit 4Handling User Events & Assistant Bots

Assistant bots overview · Monitoring system event triggers · Hotkey trigger handling · Mouse trigger handling · System trigger handling · Monitoring image and element triggers · Launching an assistant bot via keyboard event · Exception handling and debugging · Debugging tools for RPA · Strategies for solving issues · Error catching techniques

Unit 5Deploying and Maintaining Bot

Publishing bots using publish utility · Creating an RPA server · Controlling bots via server · Provisioning a robot from server · Connecting robot to server · Deploying robot to server · Publishing and managing updates · Managing packages · Uploading packages · Deleting packages

Unit 1Introduction to Remote Sensing and AI

Definition of Remote sensing · Principles of Remote Sensing · Introduction to Artificial Intelligence · Integration of AI in Remote Sensing

Unit 2Types of Remote Sensing Platforms and Sensors

Types and classification of sensors · Types of platforms (ground, airborne, space‑borne) · Data acquisition techniques and image resolution · Data pre‑processing and feature extraction techniques · Radiometric and geometric corrections · Data fusion and enhancement methods

Unit 3AI for Remote Sensing

Supervised learning for remote sensing analysis · Unsupervised learning for remote sensing analysis · Deep learning for remote sensing analysis · Image classification techniques in remote sensing

Unit 4AI‑Assisted Image Interpretation and Feature Extraction

Object detection and tracking in remote sensing imagery · Change detection and time‑series analysis · Hyperspectral and LiDAR data analysis · Fusion of multi‑modal remote sensing data

Unit 5Applications of AI in Remote Sensing

Land cover and land use mapping · Environmental monitoring and assessment · Disaster management and response · Precision agriculture and crop monitoring

Unit 1Introduction to Virtual Reality

Introduction to Virtual Reality · VR Basics · History of VR · VR paradigms · Collaboration · Virtual reality systems · Representation · User interaction · Geometry of Virtual Worlds · Geometric Models · Changing Position and Orientation · Axis · Angle Representations of Rotation · Viewing Transformations · Chaining the Transformations

Unit 2Motion in Real and Virtual Worlds

Motion in Real and Virtual Worlds · Velocities and Accelerations · The Vestibular System · Physics in the Virtual World · Mismatched Motion and Vection

Unit 3Applying Virtual Reality

Applying Virtual Reality · Virtual reality: the medium · Form and genre · What makes an application a good candidate for VR · Promising application fields · Demonstrated benefits of virtual reality · More recent trends in virtual reality application development · A framework for VR application development

Unit 4Augmented Reality Foundations

Augmented Reality · Terminology · Simple augmented reality · Augmented reality as an emerging technology · Augmented reality applications · Marker detection · Marker pose · Marker types and identification · Template markers · 2D bar‑code markers · Image markers · Infrared markers · Miniature markers · Discussion on marker use · General marker detection application

Unit 5AR Development & Applications

AR Development & Applications · User interfaces · Avoiding physical contacts · Practical experiences with head‑mounted displays · Authoring and dynamic content · AR applications and future visions · Designing an AR application · Technology adoption and acceptance · Where to use augmented reality

Unit 1Innovation definition and classification

Basic definition and classification of innovation · Relationship between innovation and entrepreneurship · Creation of competitive advantage through innovation · Innovative models · Product innovation · Process innovation · Organizational innovation · Marketing innovation · Role of innovation in business development

Unit 2Sources and creative methods in innovation

Sources of innovation (push, pull, analogies) · Technology transfer · Creative methods and approaches in innovation management · Agile management · Six Thinking Hats technique · NUF test

Unit 3Project approaches and Open Innovation

Project approach to innovation management · Stage‑Gate method and its essence · Adapting access to selected business models · In‑house business development of the innovation process · Open Innovation concept · Limits and benefits of Open Innovation for business development

Unit 4Human‑centered innovation and strategies

Human‑centered innovations · Role of co‑creation in the innovation process · Innovation process strategies · Types of innovation strategies · Selection of appropriate strategies

Unit 5Measurement, barriers, and workshops

Measurement and evaluation of innovation benefits (financial and non‑financial metrics) · Combination and selection of metrics · Barriers to innovation in business · Causes of innovation failure · Post‑audit of innovative projects · Organization and facilitation of innovation workshops