Simple Linear Regression and Correlation: Introduction to Linear Regression, The Simple Linear Regression Model, Least Squares and the Fitted Model, Properties of Least Squares Estimators, Inferences Regarding the Regression Coefficients, Prediction, Simple Linear Regression Case Study Random Variables and Probability Distributions: Concept of a random variable, discrete probability distributions, continuous probability distributions, statistical independence. Continuous probability distributions: normal distribution, areas under the normal curve, applications of the normal distribution, normal approximation to the binomial, fundamental sampling distributions: random samples, sampling distributions, sampling, distribution of means and the central limit theorem, sampling distribution of S2, t–distribution, F distribution. Introduction: Learning – Types of machine learning – Supervised learning – The brain and the neuron – Design a learning system – Perspectives and issues in machine learning – Concept learning task – Concept learning as search – Finding a maximally 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 Dataǁ, First Edition, Cambridge University Press, 2012. Ethem Alpaydin, ―Introduction to Machine Learning 3e (Adaptive Computation and Machine Learning Series), Third Edition, MIT Press, 2014. Gain an introductory understanding of the basics of computational neuroscience (including extensions to parallel distributed processing (PDP), connectionist and artificial neural network models).
Write a Python program for the class, Flower, that has three instance variables of type str, int, and float, representing the name of the flower, the number of petals, and the price, respectively. Write a simple program that allows users to create polygons of different types and enter their geometric dimensions. The program then displays their area and perimeter. The purpose of this lab is to get an overview of the different machine learning 2.
Discover interesting patterns, analyze supervised and unsupervised models, and estimate the accuracy of the algorithms. Course Objectives The aim of the course is to study the techniques to model, analyze and understand large-scale social media, together with dynamic processes through social and information networks, and to understand the relationship between qualitative and quantitative methods of social media mining. VF table, iterative calculation of the VE *VF table, EM: study of the progress in parameter values UNIT - III.
Course Objectives: The aim of the course is to study the techniques to model, analyze and understand large-scale social media, together with dynamic processes through social and information networks, and to understand the relationship between qualitative and quantitative methods of social media mining.
Resources
A brief history of dialog systems, Modern dialog systems, Modeling of conversational dialog systems, Design and development of dialog systems. Rule-Based Dialog Systems: Architecture, Methods, and Tools: Typical Dialog System Architecture, Dialog System Design, Dialog System Development Tools, Rule-Based Techniques in Dialog Systems Participating in the Alexa Award. Statistically Based Dialog Systems: Motivating a Statistically Based Approach, Dialog Components in a Statistically Based Approach, Reinforcement Learning (RL), Representing Dialog as a Markov Decision Process, From MDP to POMDP, Tracking State of Dialogue, Politics of Dialogue, Problems and Problems with Reinforcement Learning in POMDP.
Evaluating dialogue systems: how to conduct the evaluation, evaluating task-oriented dialogue systems, evaluating open domain dialogue systems, evaluation frameworks - PARADISE, quality of experience (QoE), interaction quality, best way to evaluate dialogue systems. End-to-end Neural Dialogue Systems: Neural Network Approaches to Dialogue Modeling, A Neural Conversation Model, Introduction to Neural Dialogue Technology, Retrieval-Based Response Generation, Task-Oriented Neural Dialogue Systems, Open Domain Neural Dialogue Systems, Some Issues and Current 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.
Introduction to intellectual property: Introduction, types of intellectual property, international organizations, agencies and treaties, importance of intellectual property rights. The new development of intellectual property: new developments in trademark law; copyright law, patent law, intellectual property audits. Intellectual property right – Liberating the knowledge economy, prabuddha ganguli, Tata McGraw Hill Publishing company ltd.
Familiarize yourself with general and state-of-the-art techniques used in the design and analysis of fault-tolerant digital systems. Introduction to Fault-Tolerant Computing: Basic Concepts and Course Overview; Faults and their manifestations, fault/fault modelling, reliability, availability and maintainability analysis, system evaluation, operational reliability trade-offs. Software fault tolerance: fault-free integrated circuit design and testing, fault modeling, embedded self-test, data compression, error-correcting codes, simulation software/hardware, fault-tolerant system design, CAD tools for design for testability.
Compare alternative intrusion detection tools and approaches with quantitative analysis to determine the best tool or approach to mitigate intrusion risk. Identify and describe the components of all intrusion detection systems and characterize new and emerging IDS technologies in terms of the basic capabilities common to all intrusion detection systems. State of threats to computers and network systems - Overview of computer security solutions and why they fail - Vulnerability assessment, firewalls, VPN - Overview of intrusion detection and intrusion prevention, network and host IDS.
Create and manage shared folders with an operating system, the importance of a forensic mindset, define the workload of law enforcement, explain what a typical case would look like, determine who needs to be notified of a crime, parts of evidence collection, define and apply probable cause. Synchronization in Distributed Cyber-Physical Systems: Challenges in Cyber-Physical Systems, Complexity Reduction Techniques for Synchronization, Formal Software Engineering, Distributed Consensus Algorithms, Lockstep Synchronous Implementations, Time Triggered Architecture, Related Technology, Advanced Techniques.
