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Machine Learning Techniques AKTU Quantum PDF
Unit-1
OVERVIEW: Learning, Learning Types, Clearly Stated Learning Problems, Creating a Learning System, History of Machine Learning, An introduction to machine learning approaches, including artificial neural networks, clustering, genetic algorithms, decision tree learning, reinforcement learning, Bayesian networks, support vector machines, and reinforcement learning; problems with machine learning and data science versus machine learning;
Unit-2
REGRESSION: Direct Relapse and Calculated Relapse.
BAYESIAN LEARNING – Bayes hypothesis, Idea learning, Bayes Ideal Classifier, Gullible Bayes classifier, Bayesian conviction organizations, EM calculation.
SUPPORT VECTOR MACHINE: Presentation, Sorts of help vector part – (Straight piece, polynomial portion, and Gaussian bit), Hyperplane – (Choice surface), Properties of SVM, and Issues in SVM.
Unit-3
DECISION TREE LEARNING – Choice tree learning calculation, Inductive inclination, Inductive
surmising with choice trees, Entropy and data hypothesis, Data gain, ID-3
Calculation, Issues in Choice tree learning.
Case BASED LEARNING – k-Closest Neighbor Learning, Privately Weighted
Relapse, Outspread premise capability organizations, Case-based learning.
Unit-4
Perceptrons, multilayer perceptrons, gradient descent and the delta rule, multilayer networks, backpropagation algorithm derivation, generalization, and unsupervised learning using the SOM algorithm and its version are examples of artificial neural networks.
Introduction, idea of convolutional neural networks, types of layers (activation functions, pooling, fully connected, and convolutional layers) are covered in DEEP LEARNING. Idea of layers for convolution (1D and 2D), network training, CNN case studies on topics like developing a smart speaker, self-driving cars, diabetic retinal disease, etc.
Unit-5
REINFORCEMENT LEARNING: Overview of Reinforcement Learning, Learning Assignment, Real-World Example of Reinforcement Learning Reinforcement learning models include the Markov Decision Process, Q Learning Algorithm, Q Learning Function, and Application of Reinforcement Learning. Additionally, a Deep Q Learning Introduction is provided.
Introduction, Parts, GA Reproduction Cycle, Crossover, Mutation, Genetic Programming, Evolution and Learning Models, Applications, Genetic Algorithms.
Conclusion
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