BCS055 Machine Learning Techniques Syllabus
AKTU B.Tech · 3rd Year · Unit-wise syllabus (effective 2024-25)
BCS055 Machine Learning Techniques (also written as KCS055) is a subject in the 3rd year of the AKTU B.Tech programme, prescribed by Dr. A.P.J. Abdul Kalam Technical University. The syllabus is divided into 5 units: SUPPORT VECTOR MACHINE, GENETIC ALGORITHMS. The complete unit-wise topic list, course outcomes and reference books are given below, taken from the official AKTU syllabus effective from 2024-25.
BCS055 Subject Details
| Subject Code | BCS055 / KCS055 |
|---|---|
| Subject Name | Machine Learning Techniques |
| University | Dr. A.P.J. Abdul Kalam Technical University (AKTU) |
| Course | B.Tech (Bachelor of Technology) |
| Year / Semester | 3rd Year |
| Total Units | 5 |
| Scheme Effective From | 2024-25 |
| Last Verified | 2026-08-17 |
BCS055 Machine Learning Techniques Unit-Wise Syllabus
Official AKTU syllabus (effective 2024-25) — 5 units.
Unit 1
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; REGRESSION: Linear Regression and Logistic Regression
Unit 2: SUPPORT VECTOR MACHINE
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 Gaussiankernel), Hyperplane – (Decision surface), Properties of SVM, and Issues in SVM.
Unit 3
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.
Unit 4
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.
Unit 5: GENETIC ALGORITHMS
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.
BCS055 Course Outcomes
- At the end of course , the student will be able:
- CO 1 To understand the need for machine learning for various problem solving K1 , K2
- CO 2 K1 , K3
- CO 3 To understand a wide variety of learning algorithms and how to evaluate models generated K2 , K3
- CO 4 from data K4 , K6
- CO 5 K4, K5
- To understand the latest trends in machine learning 3-0-0
- Unit Proposed
- To design appropriate machine learning algorithms and apply the algorithms to a real-world Lecture
- problems
- To optimize the models learned and report on the expected accuracy that can be achieved by
- applying the models
BCS055 Reference Books
- Tom M. Mitchell, ―Machine Learning, McGraw-Hill Education (India) Private Limited, 2013.
- Ethem Alpaydin, ―Introduction to Machine Learning (Adaptive Computation and
- Stephen Marsland, ―Machine Learning: An Algorithmic Perspective, CRC Press, 2009.
- Bishop, C., Pattern Recognition and Machine Learning. Berlin: Springer-Verlag.
Frequently Asked Questions about BCS055
What is the syllabus of BCS055 Machine Learning Techniques?
The AKTU BCS055 Machine Learning Techniques syllabus is divided into 5 units: SUPPORT VECTOR MACHINE; GENETIC ALGORITHMS. The complete topic list for each unit is published on this page, taken from the official Dr. A.P.J. Abdul Kalam Technical University syllabus effective from 2024-25.
How many units are there in BCS055?
BCS055 Machine Learning Techniques has 5 units in the AKTU syllabus.
Is BCS055 the same subject as KCS055?
Yes. AKTU renumbered its B.Tech subject codes from the 2022-23 session onwards, so the same subject appears as BCS055 and KCS055 depending on the scheme year. The syllabus content is the same.
Where can I download BCS055 previous year question papers?
AKTU B.Tech previous year question papers for every semester are available to download free on Ryzenstudy, with no login or registration required.
Is this the official AKTU syllabus for BCS055?
Yes. Every topic on this page is taken from the official Dr. A.P.J. Abdul Kalam Technical University syllabus document (AKTU B.Tech 3rd Year CS / Computer Engineering / CSE Syllabus), effective from 2024-25. A direct link to the original AKTU PDF is provided at the top of this page. Last verified on 2026-08-17.
Related AKTU Syllabus
Source: Dr. A.P.J. Abdul Kalam Technical University official syllabus — AKTU B.Tech 3rd Year CS / Computer Engineering / CSE Syllabus, effective from 2024-25. Ryzenstudy is not affiliated with AKTU. Content last verified on 2026-08-17.