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MLT AKTU Quantum PDF for 2024 Exam

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Machine Learning Techniques Aktu Quantum Pdf

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Machine Learning Techniques Quantum Notes Topics

Unit-1: Introduction

INTRODUCTION – Learning, Types of Learning, Well defined learning problems,
Designing a Learning System, History of ML, Introduction of Machine Learning Approaches
– (Artificial Neural Network, Clustering, Reinforcement Learning, Decision Tree Learning,
Bayesian networks, Support Vector Machine, Genetic Algorithm), Issues in Machine
Learning and Data Science Vs Machine Learning;

Unit-2:

REGRESSION: Linear Regression and Logistic Regression
BAYESIAN LEARNING – Bayes theorem, Concept learning, Bayes Optimal Classifier,
Naïve Bayes classifier, Bayesian belief networks, EM algorithm.
SUPPORT VECTOR MACHINE: Introduction, Types of support vector kernel – (Linear
kernel, polynomial kernel,and Gaussiankernel), Hyperplane – (Decision surface), Properties
of SVM, and Issues in SVM.

Unit-3:

DECISION TREE LEARNING– Decision tree learning algorithm, Inductive bias, Inductive
inference with decision trees, Entropy and information theory, Information gain, ID-3
Algorithm, Issues in Decision tree learning.
INSTANCE-BASED LEARNING – k-Nearest Neighbour Learning, Locally Weighted
Regression, Radial basis function networks, Case-based learning.

Unit-4:

ARTIFICIAL NEURAL NETWORKS – Perceptron’s, Multilayer perceptron, Gradient
descent and the Delta rule, Multilayer networks, Derivation of Backpropagation Algorithm,
Generalization, Unsupervised Learning – SOM Algorithm and its variant;
DEEP LEARNING – Introduction,concept of convolutional neural network , Types of layers
– (Convolutional Layers , Activation function , pooling , fully connected) , Concept of
Convolution (1D and 2D) layers, Training of network, Case study of CNN for eg on Diabetic
Retinopathy, Building a smart speaker, Self-deriving car etc.

Unit-5:

REINFORCEMENT LEARNING–Introduction to Reinforcement Learning , Learning
Task,Example of Reinforcement Learning in Practice, Learning Models for Reinforcement –
(Markov Decision process , Q Learning – Q Learning function, Q Learning Algorithm ),
Application of Reinforcement Learning,Introduction to Deep Q Learning.
GENETIC ALGORITHMS: Introduction, Components, GA cycle of reproduction,
Crossover, Mutation, Genetic Programming, Models of Evolution and Learning,
Applications.