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This interview (Artificial Intelligence and Machine Learning Interview) has been extracted from the ‘Information Technology Interview Simulator and Trainer’ that has 48 topics and over 5000 questions embedded into the Interview Simulator. For example, Machine Learning, Learning versus Designing, Training versus Testing, Characteristics of Machine learning tasks, Predictive and descriptive tasks, Machine learning Models: Geometric Models, Logical Models, Probabilistic Models. Features: Feature types, Feature Construction and Transformation, Feature Selection. Binary Classification, Assessing Classification performance, Class probability Estimation, Assessing class probability Estimates, Multiclass Classification, Regression, Assessing performance of Regression, Error measures, Overfitting, Catalysts for Overfitting, Case study of Polynomial Regression, Theory of Generalization, Effective number of hypothesis, Bounding the Growth function, VC Dimensions, Regularization theory, Least Squares method, Multivariate Linear Regression, Regularized Regression, Using , Distance Based Models, Neighbours and Examples, Nearest Neighbours Classification, Distance based clustering-K means Algorithm, Hierarchical clustering, Rule Based Models, Rule learning for subgroup discovery, Association rule mining, Tree Based Models, Decision Trees, Ranking and Probability estimation Trees, Regression trees, Clustering Trees, Probabilistic models, Normal Distribution and Its Geometric Interpretations, Naïve Bayes Classifier, Discriminative learning with Maximum likelihood, Probabilistic Models with Hidden variables: Estimation-Maximization Methods, Gaussian Mixtures, and Compression based Models, Bagging and Boosting, Multitask learning, Online learning and Sequence Prediction, Data Streams and Active Learning, Deep Learning, Reinforcement Learning, Artificial Intelligence, What Is An AI Techniques, The Level Of The Model, Criteria For AI Success, Defining The Problems As A State Space Search, Production Systems, Production Characteristics, Hill Climbing, Best-First Search, Problem Reduction, Constraint Satisfaction, Means-Ends Analysis, Knowledge Representation Issues, Representations And Mappings, Approaches To Knowledge Representation, Representation of Simple Facts In Logic, Representing Instance And Isa Relationships, Computable Functions And Predicates, How to Represent Knowledge Using Rules, Procedural Versus Declarative Knowledge, Logic Programming, Forward Versus Backward Reasoning, Symbolic Reasoning Under Uncertainty, Non-monotonic Reasoning, Logics Bayesian Networks, Dempster-Shafer Theory, Fuzzy Logic, Weak Hierarchical Planning, Reactive Systems, Other Planning Techniques, Natural Language Processing, Syntactic Processing, Semantic Analysis, Semantic Analysis, Discourse And Pragmatic Processing, Connectionist Models, Hopfield Networld, Learning In Neural Networld, Application Of Neural Networks, Recurrent Networks, Distributed Representations, Connectionist AI And Symbolic AI, Expert Systems, Explanation Facilities, Expert System Developments Process, knowledge Acquisition, Prolog, Syntax and Numeric Function, Basic List Manipulation Functions In Prolog, Functions, Predicates and Conditional, Input, Output and Local Variables, Iteration and Recursion, Property Lists and Arrays, Miscellaneous Topics, LISP and Other AI Programming Languages.
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It is customisable! You can choose the specific topics for each of your interview/viva examinations from: Artificial Intelligence & Machine Learning, Cloud Computing, Computer Networks Technology, Database Management Systems, Design & Analysis of Algorithms, Information & Cyber Security, IT Project Management, Mobile Computing, Multimedia Technologies, Object Oriented Modelling & Design, Image Processing, Real Time Systems, Software Testing & Quality Assurance, Web Engineering & Technology, Coding Techniques, Computer Forensic & Cyber Applications, Computer Networks,
(Each of the above topics contains 80 to 110 questions.)