# Syllabus Machine Learning Techniques (KCS-055) Aktu Btech

Discover the algorithms and techniques employed for pattern recognition, data analysis, and predictive modelling by exploring the AKTU Btech syllabus on Machine Learning Techniques. Develop clever data-driven systems to their full potential.

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## UNIT-1: 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 & BAYESIAN LEARNING

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

## UNIT-3: DECISION TREE LEARNING

• 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,
• Case-based learning.

## UNIT-4: ARTIFICIAL NEURAL NETWORKS

• ARTIFICIAL NEURAL NETWORKS – Perceptron’s, Multilayer perceptron, Gradient descent & 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

• REINFORCEMENT LEARNING-Introduction to Reinforcement Learning,
• 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.