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AKTU Machine Learning Techniques (BCAI601) Syllabus PDF – B.Tech 3rd Year
Machine Learning Techniques BCAI601
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Official AKTU syllabus of Machine Learning Techniques (BCAI601) for B.Tech 3rd Year – 4 credits, L-T-P 3-1-0. Read the unit-wise course content, course outcomes, evaluation pattern and recommended books online, or download the PDF.
Syllabus — Machine Learning Techniques
Download PDFBCAI601 · 4 credits · L-T-P 3-1-0
Course Outcomes
At the end of course, the student will be able to
| CO1 | To understand the need for machine learning for various problem solving | K , K 1 2 |
| CO2 | To understand a wide variety of learning algorithms and how to evaluate models generated from data | K , K 1 3 |
| CO3 | To understand the latest trends in machine learning | K , K 2 3 |
| CO4 | To design appropriate machine learning algorithms and apply the algorithms to a realworld problems | K , K 4 6 |
| CO5 | To optimize the models learned and report on the expected accuracy that can be achieved by applying the models | K , K 4 5 |
Course Content
L-T-P: 3-0-0
| Unit | Topic | Proposed Lecture |
|---|---|---|
| I | 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; | 08 |
| II | 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 Gaussian kernel), Hyperplane – (Decision surface), Properties of SVM, and Issues in SVM. | 08 |
| III | 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 | 08 |
| IV | 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. | 08 |
| V | 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. | 08 |
Text Books
- Tom M. Mitchell, ―Machine Learning, McGraw-Hill Education (India) Private Limited, 2013.
- EthemAlpaydin, ―Introduction to Machine Learning (Adaptive Computation and Machine Learning), MIT Press
- Stephen Marsland, ―Machine Learning: An Algorithmic Perspective, CRC Press, 2009.
- Bishop, C., Pattern Recognition and Machine Learning. Berlin: Springer-Verlag.
- M. Gopal, “Applied Machine Learning”, McGraw Hill Education
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