AKTU Machine Learning Techniques (BCS055) Notes & Study Material PDF – B.Tech 3rd Year Sem 5

Complete and exam-oriented Machine Learning Techniques notes for AKTU B.Tech 3rd Year Semester V. Covers all 5 units including ML fundamentals, regression, Bayesian learning, SVM, decision trees, instance-based learning, neural networks, deep learning, reinforcement learning and genetic algorithms, with diagrams, solved examples, important questions and AKTU-pattern sample paper.

This comprehensive 180-page study material provides topic-wise, textbook-level coverage of the complete BCS-055 syllabus. The PDF includes detailed explanations, diagrams, solved numericals/examples, unit summaries, glossaries, important questions and an AKTU-pattern sample question paper.

🎓 Course Details

• Subject: Machine Learning Techniques
• Course Code: BCS-055
• Year: 3rd Year
• Semester: V
• Course Type: Departmental Elective-I
• Credits: 3
• Branches: CSE / Computer Science / Computer Engineering / Allied Branches
• Coverage: Complete 5-unit BCS-055 syllabus

📚 Complete 5-Unit Coverage

🔹 UNIT I – Introduction to Machine Learning

Topics include:

• Learning – Definitions and Perspectives
• Types of Learning
• Well-Defined Learning Problems
• Designing a Learning System
• History of Machine Learning
• Artificial Neural Networks
• Clustering
• Reinforcement Learning
• Decision Tree Learning
• Bayesian Networks
• Support Vector Machines
• Genetic Algorithms
• Issues in Machine Learning
• Data Science vs Machine Learning

The notes explain the Task–Performance–Experience (T-P-E) framework and different learning paradigms, including supervised, unsupervised, reinforcement and semi-supervised learning, with solved examples.

🔹 UNIT II – Regression & Bayesian Learning

Covers:

• Linear Regression
• Linear Regression Model & Assumptions
• Least-Squares Estimation
• Logistic Regression
• Sigmoid Function
• Maximum Likelihood Estimation
• Bayes' Theorem
• Concept Learning
• Bayes Optimal Classifier
• Naive Bayes Classifier
• Bayesian Belief Networks
• EM Algorithm

🔹 UNIT III – SVM, Decision Trees & Instance-Based Learning

Includes:

• Support Vector Machine
• Linear Kernel
• Polynomial Kernel
• Gaussian Kernel
• Hyperplane & Decision Surface
• Properties and Issues of SVM
• Decision Tree Learning Algorithm
• Inductive Bias
• Inductive Inference
• Entropy & Information Theory
• Information Gain
• ID3 Algorithm
• Issues in Decision Tree Learning
• k-Nearest Neighbour (k-NN)
• Locally Weighted Regression
• Radial Basis Function Networks
• Case-Based Learning

🔹 UNIT IV – Artificial Neural Networks & Deep Learning

Topics include:

• Perceptron
• Multilayer Perceptron
• Gradient Descent
• Delta Rule
• Multilayer Networks
• Backpropagation Algorithm
• Generalization in Neural Networks
• Self-Organizing Maps (SOM)
• Introduction to Deep Learning
• Convolutional Neural Networks
• Convolutional Layers
• Activation Functions
• Pooling
• Fully Connected Layers
• 1D & 2D Convolution
• Training of Deep Networks
• CNN Case Studies

The PDF includes case studies involving diabetic retinopathy, smart speakers and self-driving cars.

🔹 UNIT V – Reinforcement Learning & Genetic Algorithms

Covers:

•  Introduction to Reinforcement Learning
• Reinforcement Learning Task
• Practical Applications
• Markov Decision Process
• Q-Learning
• Q-Learning Algorithm
• Applications of Reinforcement Learning
• Deep Q-Learning
• Genetic Algorithms
• Components of Genetic Algorithms
• Reproduction
• Crossover
• Mutation
• Genetic Programming
• Models of Evolution and Learning
• Applications of Genetic Algorithms