BCAI601 Machine Learning Techniques Syllabus
AKTU B.Tech · 6th Semester · Unit-wise syllabus (effective 2024-25)
BCAI601 Machine Learning Techniques (also written as KCAI601) is a subject in Semester 6 of the AKTU B.Tech programme, prescribed by Dr. A.P.J. Abdul Kalam Technical University. The syllabus is divided into 5 units: INTRODUCTION, REGRESSION and 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.
BCAI601 Subject Details
| Subject Code | BCAI601 / KCAI601 |
|---|---|
| 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 · 6th Semester |
| Total Units | 5 |
| Scheme Effective From | 2024-25 |
| Last Verified | 2026-08-19 |
BCAI601 Machine Learning Techniques Unit-Wise Syllabus
Official AKTU syllabus (effective 2024-25) — 5 units.
Unit 1: INTRODUCTION
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;
Unit 2: REGRESSION · SUPPORT VECTOR MACHINE
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.
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.
BCAI601 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 K1, K2
- CO2 To understand a wide variety of learning algorithms and how to evaluate models generated from K1, K3
- data
- CO3 To understand the latest trends in machine learning K2, K3
- CO4 To design appropriate machine learning algorithms and apply the algorithms to a real-world K4, K6
- problems
- K4, K5
- CO5 To optimize the models learned and report on the expected accuracy that can be achieved by 3-0-0
- applying the models
BCAI601 Reference Books
- Tom M. Mitchell, ―Machine Learning, McGraw-Hill Education (India) Private Limited, 2013.
- EthemAlpaydin, ―Introduction to Machine Learning (Adaptive Computation and Machine Learning),
- 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
Frequently Asked Questions about BCAI601
What is the syllabus of BCAI601 Machine Learning Techniques?
The AKTU BCAI601 Machine Learning Techniques syllabus is divided into 5 units: INTRODUCTION; REGRESSION and 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 BCAI601?
BCAI601 Machine Learning Techniques has 5 units in the AKTU syllabus.
Is BCAI601 the same subject as KCAI601?
Yes. AKTU renumbered its B.Tech subject codes from the 2022-23 session onwards, so the same subject appears as BCAI601 and KCAI601 depending on the scheme year. The syllabus content is the same.
Which semester is BCAI601 taught in?
BCAI601 Machine Learning Techniques is taught in Semester 6 of the 3rd year of the AKTU B.Tech programme.
Where can I download BCAI601 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 BCAI601?
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 Artificial Intelligence & Machine Learning Syllabus), effective from 2024-25. A direct link to the original AKTU PDF is provided at the top of this page. Last verified on 2026-08-19.
Related AKTU Syllabus
Source: Dr. A.P.J. Abdul Kalam Technical University official syllabus — AKTU B.Tech 3rd Year Artificial Intelligence & Machine Learning Syllabus, effective from 2024-25. Ryzenstudy is not affiliated with AKTU. Content last verified on 2026-08-19.