3rd Year Machine Learning Technique AKTU Quantum for 2024 Exam

Download AKTU Quantum Machine Learning Technique PDF for 2024 Exam. If you are a 3rd-year B.Tech student, this is the PDF for your Machine Learning Technique. Find the appropriate location and click to receive your free PDF, which is intended for 3rd-year B.Tech students.

MLT AKTU Quantum PDF for 2024 Exam

With the state-of-the-art Machine Learning exam strategies from AKTU Quantum, you can maximize the potential of your academic career.

Gain a thorough understanding of ML ideas to empower yourself and succeed in tests and other endeavors. Get a competitive edge by delving into the complexities of algorithms, predictive modeling, and data analysis.

Exam criteria are easily aligned with AKTU Quantum’s personalized approach to machine learning education, giving you the skills you need to succeed. Improve your performance with a curriculum developed to help you achieve your goals and fulfill industry demands.

With AKTU Quantum, embrace the future of education and gain machine learning skills for a more promising academic future.

Machine Learning Techniques Aktu Quantum Pdf

Download Machine Learning Techniques Aktu quantum pdf free download:- Download

Download all AKTU Quantum pdf Series 3rd year CSE:- Download

Download All CSE aktu quantum pdf:- Download

If you are looking for all AKTU Quantum pdf and Notes PDF downloads:- Click Here

We will also upload Machine Learning Techniques handwritten notes

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;


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.


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.


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.


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,

Leave a Comment