Aktu Machine Learning (KOE-073) Btech Syllabus

Explore the AKTU B.Tech Machine Learning syllabus, including algorithms, statistical models, and data analysis techniques for training intelligent systems. Learn about automated pattern detection and prediction.

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UNIT-1: INTRODUCTION 

• INTRODUCTION – Well defined learning problems,
• Designing a Learning System,
• Issues in Machine Learning;
• THE CONCEPT LEARNING TASK -General-to-specific ordering of hypotheses,
• Find-S,
• List then eliminate algorithm,
• Candidate elimination algorithm,
• Inductive bias. 

UNIT-2: DECISION TREE LEARNING

• DECISION TREE LEARNING
• Decision tree learning algorithm Inductive bias- Issues.
• In Decision tree learning;
• ARTIFICIAL NEURAL NETWORKS  Perceptrons,
• Gradient descent and the Delta rule,
• Adaline, Multilayer networks,
• Derivation of backpropagation rule Backpropagation Algorithm Convergence,
• Generalization. 

UNIT-3: EVALUATING HYPOTHESES

• Evaluating Hypotheses: Estimating Hypotheses Accuracy,
• Basics of sampling Theory,
• Comparing Learning Algorithms;
• Bayesian Learning: Bayes theorem,
• Concept learning,
• Bayes Optimal Classifier,
• Naive Bayes classifier,
• Bayesian belief networks,
• EM algorithm.

UNIT-4: COMPUTATIONAL LEARNING THEORY

• Computational Learning Theory: Sample Complexity for Finite Hypothesis spaces,
• Sample Complexity for Infinite Hypothesis spaces,
• The Mistake Bound Model of Learning;
• INSTANCE-BASED LEARNING  k-Nearest Neighbour Learning,
• Locally Weighted Regression,
• Radial basis function networks,
• Casebased learning. 

UNIT-5: GENETIC ALGORITHM

• Genetic Algorithms: an illustrative example,
• Hypothesis space search,
• Genetic Programming,
• Models of Evolution and Learning;
• Learning first order rules sequential covering algorithms
• General to specific beam search-FOIL;
• REINFORCEMENT LEARNING
• The Learning Task,
• Q Learning. 

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