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AKTU Machine Learning (BOE073) Syllabus PDF – B.Tech Open Elective
Machine Learning BOE073
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Official AKTU syllabus of Machine Learning (BOE073) for B.Tech Open Elective – 3 credits, L-T-P 3-0-0. Read the unit-wise course content, course outcomes, evaluation pattern and recommended books online, or download the PDF.
Syllabus — Machine Learning
Download PDFBOE073 · 3 credits · L-T-P 3-0-0
Course Objective: The objective of this course is to provide a comprehensive understanding of core machine learning concepts, algorithms, and models, enabling students to design, implement, and evaluate learning systems across various paradigms including neural networks, Bayesian methods, and reinforcement learning.
After completion of the course student will be able to:
| COs | CO Statement | BL |
|---|---|---|
| CO1 | Understand and analyze fundamental concepts of machine learning, including the design of learning systems and the role of hypothesis space and inductive bias. | K2 |
| CO2 | Apply and evaluate concept learning algorithms such as Find-S, Candidate Elimination, and decision tree learning, while addressing related issues and biases. | K4 |
| CO3 | Design and implement artificial neural networks using perceptrons, Adaline, and backpropagation, and assess their convergence and generalization capabilities | K3 |
| CO4 | Utilize probabilistic models including Bayesian learning, Naïve Bayes classifiers, belief networks, and EM algorithm for accurate hypothesis evaluation and prediction. | K3 |
| CO5 | Analyze and implement advanced learning paradigms such as instance-based learning, genetic algorithms, first-order rule learning, and reinforcement learning including Q-learning. | K4 |
| Unit | Content | Hours |
|---|---|---|
| I | 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. | 8 |
| II | 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. | 8 |
| III | Evaluating Hypotheses: Estimating Hypotheses Accuracy, Basics of sampling Theory, Comparing Learning Algorithms; Bayesian Learning: Bayes theorem, Concept learning, Bayes Optimal Classifier, Naïve Bayes classifier, Bayesian belief networks, EM algorithm. | 8 |
| IV | 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, Case-based learning. | 8 |
| V | 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. | 8 |
Text Books
- Tom M. Mitchell,―Machine Learning, McGraw-Hill Education (India) Private Limited, 2013.
- Ethem Alpaydin,―Introduction to Machine Learning (Adaptive Computation and Machine Learning), The MIT Press 2004.
- Stephen Marsland, ―Machine Learning: An Algorithmic Perspective, CRC Press, 2009.
- Bishop, C., Pattern Recognition and Machine Learning. Berlin: Springer- Verlag.
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