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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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Important Questions For Machine Learning Techniques:
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  • 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,
  • 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.


  • 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 & 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,
  • Applications. 
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Machine Learning Techniques Btech Quantum PDF, Syllabus, Important Questions

Subject SyllabusSyllabus
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Question paper – 2021-222021-22

Machine Learning Techniques Quantum PDF | AKTU Quantum PDF:

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Quantum -2022-232022-23

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