Practical Data Structures and Algorithms with Python: Write Complex and Powerful Code Using the Latest Features of Python 3.7, 2nd Edition by Dr. Solving Problems with Algorithms and Data Structures Using Python by Bradley N Miller and David L.Ranum. Introduction: Learning – Types of Machine Learning – Supervised Learning – The Brain and the Neuron – Designing a Learning System – Perspectives and Issues in Machine Learning – Concept Learning Task – Concept Learning as Search – Finding a Maximum Specific Hypothesis – Version Spaces and the Candidate Elimination Algorithm – Linear Discriminants: – Perceptron – Linear separability – Linear regression.
Tree and Probabilistic Models: Learning with Trees – Decision Trees – Constructing Decision Trees – Classification and Regression Trees – Collaborative Learning – Boosting – Bagging – Different Ways of Combining Classifiers – Basic Statistics – Gaussian Mixture Models – Nearest Neighbor Methods – Unsupervised Learning – K Means Algorithms . Dimensionality Reduction and Evolutionary Models: Dimensionality Reduction – Linear Discriminant Analysis – Principal Component Analysis – Factor Analysis – Independent Component Analysis – Local Linear Embedding – Isomap – Least Squares Optimization – Evolutionary Learning – Genetic Algorithms – Genetic Descendants: - Genetic Operators – Application of Genetic Algorithms – Reinforcement Learning – Overview – Loss Example. Stephen Marsland, ―Machine Learning – An Algorithmic Perspective, Second Edition, Chapman and Hall/CRC Machine Learning and Pattern Recognition Series, 2014.
Peter Flach, ‘Machine Learning: The Art and Science of Algorithms that Make Sense of Data’, eerste editie, Cambridge University Press, 2012. Ethem Alpaydin, ‘Introduction to Machine Learning 3e (Adaptive Computation and Machine Learning Series), derde editie, MIT Pers, 2014.
Tech DS I Year I Sem
Introduction to social media: World Wide Web, Web 1.0, Web 2.0, Web 3.0, social media, core features of social media, types of social media, social networking sites, use of Facebook for business purposes, content communities. Traditional Business Analytics, Seven Layers of Social Media Analytics, Types of Social Media Analytics, Social Media Analytics Cycle, Social Media Analytics Challenges, Social Media Analytics Tools. Social Media Text Analytics: Types of social media text, purpose of text analysis, steps in text analysis, social media text analysis tools.
Social Media Actions Analytics: Introduction to Actions Analytics, general social media actions, Actions Analytics Tools. Social Media Hyperlink Analysis: Types of Hyperlinks, Hyperlink Analysis, Types of Hyperlink Analysis, Hyperlink Analysis Tools. Seven Layers of Social Media Analytics That Collect Business Insights from Social Media Text, Actions, Networks, Hyperlinks, Apps, Search Engine and Location Data by Gohar F.
Social Media Analytics: Techniques and Insights to Get Business Value from Social Media Matthew Ganis, Avinash Kohirkar, Pearson Education. The goal of this lab is to gain an overview of different types of machine learning. This course aims to provide students with knowledge of the principles and techniques of big data analytics.
This course also aims to expose the limits of the results of Big data Analytics courses. Ability to explain the foundations, definitions and challenges of Big Data and various analytical tools. History of Data Management - Evolution of Big Data, Structuring Big Data, Elements of Big Data, Big Data Analytics, Careers in Big Data, Future of Big Data.
Technologies for handling big data: distributed and parallel computing for big data, introduction of Hadoop, cloud computing and big data, in-memory computing technology for big data. Social media analysis and text mining: introducing social media, introducing the key elements of social media, introducing text mining, understanding the text mining process, sentiment analysis, conducting social media analysis and opinions. Big Data Analytics: Disruptive Technologies to Change the Game, Arvind Sathi, 1st Edition, IBM Corporation, 2012.
This course is also designed to introduce the frontiers of big data analytics. Using R to Unlock the Value of Big Data: Big Data Analytics with Oracle R Enterprise and.
Resources
Knowledge of digital forensics legislation, digital crime, forensic processes and procedures, data collection and validation, e-discovery tools. Create and manage shared folders using operating system, the importance of the forensic mindset, define the law enforcement workload, Explain what the normal case will look like, Define who should be notified of a crime, parts of evidence collection, Define and apply probable cause. Computer Forensics: Prepare a case, Begin an investigation, Understand computer forensics workstations and software, Conduct an investigation, Complete a case, Critique a case.
William Oettinger, Learn Computer Forensics: A Beginner's Guide to Searching, Analyzing, and Securing Digital Evidence, Packt Publishing; First Edition (April 30, 2020), ISBN. A brief history of dialog systems, today's dialog systems, modeling of conversational dialog systems, design and development of dialog systems. Rule-Based Dialogue Systems: Architecture, Methods and Tools: A Typical Architecture of Dialogue Systems, Designing a Dialogue System, Tools for Developing Dialogue Systems, Rule-Based Techniques in Award-Participating Dialogue Systems Alexa.
Statistical data-driven dialogue systems: Motivation of the statistical data-driven approach, Components of dialogue in the statistical data-driven approach, Reinforcement Learning (RL), Representation of dialogue as a Markov decision-making process, by MDP- of POMDPs tracking policy, , Problems and issues with reinforcement learning in POMDPs. Dialog Systems Evaluation: How to Perform Evaluation, Task Oriented Dialog Systems Evaluation, Open Domain Dialog Systems Evaluation, Evaluation Frameworks - PARADISE, Quality of Experience (QoE), Interaction Quality, How to good for evaluating dialogue systems. End-to-End Neural Dialogue Systems: Neural Network Approaches to Dialogue Modeling, A Neural Conversational Model, Introduction to Neural Dialogue Technology, Repetition-Based Response Generation, Task-Oriented Neural Dialogue Systems, Some Systems of Open Domain Neural Dialogue, Current Issues and Solutions, Dialogue Systems: Datasets, Competitions, Tasks and Challenges.
Michael McTear, “Conversational AI: Dialogue Systems, Conversational Agents, and Chatbots,” Second Edition, Moran and Claypool Publishers, 2020. Establishing essential coverage of service-oriented architecture models and the underlying design paradigm, along with documentation of the methodology. Understanding Service Orientation: Introduction to Service Orientation, Problems Solved by Service Orientation, Effects of Service Orientation on the Business, Objectives and Benefits of Service Oriented Computing, Four Pillars of Service Orientation.
Principles of service orientation: profile for the principle of standardized service contract, profile for the principle of service loose coupling, profile for the principle of service abstraction, profile for the principle of service reusability, profile for the principle of service autonomy, profile for the principle of Service Statelessness, Profile for the principle of Service Discoverability, Profile for the principle of Service Composability (Appendix-A of the textbook). Understanding SOA Architectures: Introduction to SOA, Four Characteristics of SOA, Four Common Types of SOA, End Result of Service Orientation and SOA, SOA Project and Life Cycle Phases. Service-oriented analysis and design: Web service modeling process, business process breakdown (into granular actions), filtering irrelevant actions, defining entity service candidates, identifying process-specific logic, applying service orientation, identifying service composition candidates, analyzing Requirements processing , defining candidates for utility services, defining candidates for microservices, using service orientation, auditing candidates for service composition, auditing clustering candidates for capabilities.
Unit 1
Gain an understanding of how managers use business analytics to design and solve business problems and to support managerial decision-making. Analyze and solve problems from various industries such as manufacturing, services, retail, software, banking and finance, sports, pharmaceuticals, aerospace, etc. Students will demonstrate the ability to apply technical skills in predicative and prescriptive modeling to support business decision making.
Unit 2
Unit 3
Unit 4
Unit 5
Course objectives
Understand that how to improve your writing skills and level of readability 2. Learn about what to write in each section
Understand the skills needed when writing a Title
UNIT - I
UNIT - II
UNIT - III
UNIT - IV
UNIT - V
Suggested Studies
Adrian Wallwork, English for Writing Research Papers, Springer New York Dordrecht Heidelberg London, 2011
UNIT-I
UNIT-II
UNIT-III
Disaster Prone Areas In India Study Of Seismic Zones; Areas Prone To Floods And Droughts, Landslides And Avalanches; Areas Prone To Cyclonic And Coastal Hazards With Special
UNIT-IV
Disaster Preparedness And Management Preparedness: Monitoring Of Phenomena Triggering A Disaster Or Hazard; Evaluation Of Risk: Application Of Remote Sensing, Data From
UNIT-V
SUGGESTED READINGS
Objectives
To get a working knowledge in illustrious Sanskrit, the scientific language in the world 2. Learning of Sanskrit to improve brain functioning
The engineering scholars equipped with Sanskrit will be able to explore the 6. huge knowledge from ancient literature
UNIT - II Order
Suggested Reading
- Tech DS I Year ISem./ II Sem L T P C
- Understand value of education and self- development 2. Imbibe good values in students
- Let the should know about the importance of character
- Tech DS I Year ISem./ II Sem L T P C 2 0 0 0
- Understand the premises informing the twin themes of liberty and freedom from a civil rights perspective
- To address the growth of Indian opinion regarding modern Indian intellectuals’ constitutional role and entitlement to civil and economic rights as well as the emergence of nationhood in the early years of
- To address the role of socialism in India after the commencement of the Bolshevik Revolution in 1917 and its impact on the initial drafting of the Indian Constitution
- The Constitution of India, 1950 (Bare Act), Government Publication
- Tech DS I Year ISem./ II Sem L T P C
- Tech DS I Year ISem./ II Sem L T P C Prerequisite:None 2 0 0 0
Values and Ethics for Organizations Theory and Practice”, Oxford University Press, New Delhi Press, New Delhi. To trace the growth of Indian opinion regarding modern Indian intellectuals' constitutional role and claim to civil and economic rights, as well as the emergence of nationhood in the early years of right to civil and economic rights, as well as the emergence of nationhood in addressing the early years. of Indian nationalism. To address the role of socialism in India after the onset of the Bolshevik Revolution in 1917 and its impact on the initial drafting of the Indian Constitution 1917 and its impact on the initial drafting of the Indian Constitution.
History of the Making of the Indian Constitution: History Drafting Committee, (Composition and Working) Philosophy of the Indian Constitution: Preamble Salient Features. Outlines of Constitutional Rights and Duties: Fundamental Rights Right to Equality Right to Liberty Right Against Exploitation Right to Freedom of Religion Cultural and Educational Rights Right to Constitutional Remedies Directive Principles of State Policy Fundamental Duties. Governing bodies: Composition of Parliament Qualifications and disqualifications Powers and functions Executive President Governor Council of Ministers Judiciary, appointment and transfer of judges, qualifications powers and functions.
Block level: organizational hierarchy (different departments), village level: role of elected and appointed officials, importance of grassroots democracy. Introduction and methodology: Objectives and rationale, Policy background, Conceptual framework and terminology Theories of learning, Curriculum, Teacher education. How can teacher training (curriculum and practicum) and the school curriculum and guidance materials best support effective pedagogy?
Professional Development: Alignment with classroom practice and follow-up support, Peer support, Principal and community support.
