Program Elective-4 — What This Subject Is About
Program Elective-4 is the 6th semester's specialisation choice — one theory elective worth 100 marks and 4 credits, and you pick the direction that fits you. Option 6.1.1 is Fundamentals of Artificial Intelligence: how machines search, plan, reason and play games. Option 6.1.2 is Data Science and Machine Learning: how raw data is cleaned, explored and turned into predictions. You choose ONE of the two, and you are examined only in the option you choose.
Both options carry the same weight — 42 periods, 100 marks, 4 credits — so the choice is about interest and career, not marks. Artificial Intelligence suits you if you like algorithms, logic and problem-solving; Data Science and Machine Learning suits you if you like data, statistics and building models that learn from examples. This post covers both options completely, in the official order.
Subject 6.1 (Program Elective-4) • Total 100 Marks • 4 Credits • L 4 • Choose ONE of 6.1.1 / 6.1.2 • 6th Semester — Computer Science & Engineering • Session 2026–27
About this document: Program Elective-4 is a theory elective worth 4 credits and 100 marks. Students choose ONE of the two options below — 6.1.1 Fundamentals of Artificial Intelligence or 6.1.2 Data Science and Machine Learning — and are examined only in the option they choose. Both options are covered here, completely, in the official order.
6.1.1 — Fundamentals of Artificial Intelligence
Identify problems where artificial intelligence techniques are applicable; apply selected basic AI techniques; judge applicability of more advanced techniques; participate in the design of systems that act intelligently and learn from experience.
Course Outcomes
- CO1: Understanding the basic concepts of Artificial Intelligence.
- CO2: Describing different AI searching algorithms.
- CO3: Applying various optimization techniques to real world problems.
- CO4: Capable of implementing autonomous planning under constraints in simulated or real environment.
- CO5: Applying different AI logics to solve various problems.
Units — 6.1.1 Fundamentals of Artificial Intelligence
Unit 1 — Introduction (8 Periods, 20%)
Overview and Historical Perspective, Turing test, Physical Symbol Systems and the scope of Symbolic AI, Agents.
What AI is, how the idea started, the Turing test, and what an intelligent agent does.
Unit 2 — Searching (8 Periods, 20%)
Heuristic Search: Best First Search, Hill Climbing, Beam Search, Tabu Search. Randomized Search: Simulated Annealing, Genetic Algorithms, Ant Colony Optimization.
How a computer searches for answers — trying paths smartly instead of checking everything blindly.
Unit 3 — Finding Optimal Paths, Problem Decomposition and Game Playing (10 Periods, 20%)
Finding Optimal Paths: Branch and Bound, A*, IDA*, Divide and Conquer approaches, Beam Stack Search. Problem Decomposition: Goal Trees, AO*, Rule Based Systems, Rete Net. Game Playing: Minimax Algorithm, Alpha-Beta Algorithm, SSS*.
Finding the best route, breaking big problems into smaller ones, and how computers play games like chess.
Unit 4 — Planning and Constraint Satisfaction (8 Periods, 20%)
Domains, Forward and Backward Search, Goal Stack Planning, Plan Space Planning, Graphplan, Constraint Propagation.
How a computer makes a plan step by step and handles rules and limits while doing it.
Unit 5 — Logic and Inferences (8 Periods, 20%)
Propositional Logic, First Order Logic, Soundness and Completeness, Forward and Backward chaining.
The logic behind reasoning — how a machine draws conclusions from facts and rules.
6.1.2 — Data Science and Machine Learning
This course will introduce the concepts of data warehouse and data mining, which gives a complete description about the principles, used, architectures, applications, design and implementation of data mining and data warehousing concepts.
Course Outcomes
- CO 1: Understand the basics of Data Science.
- CO 2: Visualize and Analyze Data.
- CO 3: Understand concept of Exploratory Data Analysis.
- CO 4: Understand and develop Machine Learning Algorithms.
- CO 5: Implement Mining Process on Social Network Graph.
Units — 6.1.2 Data Science and Machine Learning
Unit 1 — Introduction of Data Science and Machine Learning (10 Periods, 22%)
Fundamentals of Artificial Intelligence, need and applications of Data Science, Data Mining, data preparation, Machine Learning, Types and Applications of Machine Learning.
What data science and machine learning are, and where they are used.
Unit 2 — Data Preprocessing, Analysis and Visualization (6 Periods, 16%)
Data Pre-processing: Pre-processing Techniques — Mean Removal, Scaling, Normalization, Binarization, One Hot Encoding, Label encoding. Data Analyses: Loading and summarizing the dataset. Data Visualization: Univariate Plots, Multivariate Plots, Training Data, Test Data, Performance Measures.
Cleaning raw data, understanding it, and drawing charts that show what it means.
Unit 3 — Statistical Inference & Exploratory Data Analysis and the Data Science Process (10 Periods, 22%)
Populations and samples, Types of Statistical modelling, Types of probability distributions, Parametric and Non-Parametric Methods, Distance Metrics. Basic tools (plots, graphs and summary statistics) of EDA, Philosophy of EDA, The Data Science Process.
Statistics, exploring data with plots and summaries, and the step-by-step data science process.
Unit 4 — Machine Learning Algorithms (8 Periods, 20%)
Introduction to Supervised Learning Algorithms — Decision Tree, Linear Regression, k Nearest Neighbours (k-NN), SVM — and Introduction to Unsupervised Learning Algorithms — K-means Clustering, Mean Shift Algorithm, Dimensionality Reduction Techniques, Introduction to Neural Networks.
The main machine learning methods — decision trees, regression, nearest neighbours, SVM, clustering and neural networks.
Unit 5 — Mining Social-Network Graphs, Data Science and Ethical Issues (8 Periods, 20%)
Social networks as graphs, Clustering of graphs, Direct discovery of communities in graphs, Partitioning of graphs, Neighbourhood properties in graphs, Discussions on privacy, security, ethics, A look back at Data Science, Next-generation data scientists.
How social networks are studied as graphs, and the privacy, security and ethics questions data science raises.
Practical Work
No practicals are listed in the official syllabus for this subject — it is a theory subject (lectures only).
Complete Syllabus
The full examination scheme and the reference books from the official syllabus. The unit-wise topics are in the “What You Will Study” section above.
Examination Scheme
| Code | Paper | Type | L – P | Credits | Internal | External | Total |
|---|---|---|---|---|---|---|---|
| 6.1 | Program Elective-4 (choose ONE of 6.1.1 / 6.1.2) | Theory | 4 – 0 | 4 | Theory 40 | Theory 60 (3 hrs) | 100 |
| Program Elective-4 Total | 4 | 40 | 60 | 100 | |||
6.1.1 — Fundamentals of Artificial Intelligence
Units at a Glance
| Unit | Topic | Periods | Marks Share |
|---|---|---|---|
| 1 | Introduction | 8 | 20% |
| 2 | Searching | 8 | 20% |
| 3 | Finding Optimal Paths, Problem Decomposition and Game Playing | 10 | 20% |
| 4 | Planning and Constraint Satisfaction | 8 | 20% |
| 5 | Logic and Inferences | 8 | 20% |
Reference Books
As listed in the syllabus:
- Deepak Khemani. A First Course in Artificial Intelligence, McGraw Hill Education (India).
- https://nptel.ac.in/courses/106106126/
- Stefan Edelkamp and Stefan Schroedl. Heuristic Search, Morgan Kaufmann.
- Pamela McCorduck, Machines Who Think: A Personal Inquiry into the History and Prospects of Artificial Intelligence, A K Peters/CRC Press.
- Elaine Rich and Kevin Knight. Artificial Intelligence, Tata McGraw Hill.
- Stuart Russell and Peter Norvig. Artificial Intelligence: A Modern Approach, Prentice Hall.
- M.C. Trivedi, A classical approach to Artificial Intelligence, Khanna Publishing House.
6.1.2 — Data Science and Machine Learning
Units at a Glance
| Unit | Topic | Periods | Marks Share |
|---|---|---|---|
| 1 | Introduction of Data Science and Machine Learning | 10 | 22% |
| 2 | Data Preprocessing, Analysis and Visualization | 6 | 16% |
| 3 | Statistical Inference & Exploratory Data Analysis and the Data Science Process | 10 | 22% |
| 4 | Machine Learning Algorithms | 8 | 20% |
| 5 | Mining Social-Network Graphs, Data Science and Ethical Issues | 8 | 20% |
Reference Books
As listed in the syllabus:
- Jiawei Han, Micheline Kamber, Jian Pei, Data Mining: Concepts and Techniques, Elsevier.
- Margaret H Dunham, Data Mining Introductory and Advanced Topics, Pearson Education.
- Amitesh Sinha, Data Warehousing, Thomson Learning, India.
- Xingdong Wu, Vipin Kumar, the Top Ten Algorithms in Data Mining, CRC Press, UK.
Frequently Asked Questions
How many marks is Program Elective-4 worth in the 6th semester?
100 marks in total and 4 credits. Program Elective-4 (choose ONE of 6.1.1 / 6.1.2): 100 marks (internal Theory 40, external Theory 60 (3 hrs)).
Which option should I choose — Artificial Intelligence or Data Science and Machine Learning?
Both carry the same marks and credits, so choose by interest. Artificial Intelligence focuses on searching, planning, game playing and logic; Data Science and Machine Learning focuses on data preparation, statistics and learning algorithms. If you plan to work with data, analytics or ML models, take 6.1.2; if you enjoy algorithms, reasoning and intelligent systems, take 6.1.1.
Which units carry the most weightage in Artificial Intelligence (6.1.1)?
Every unit carries the same weightage — 20% each — so none of them can be skipped. The five units are Introduction, Searching, Finding Optimal Paths / Problem Decomposition / Game Playing, Planning and Constraint Satisfaction, and Logic and Inferences.
Which units carry the most weightage in Data Science and Machine Learning (6.1.2)?
Unit 1 (Introduction of Data Science and Machine Learning, 10 periods) and Unit 3 (Statistical Inference & Exploratory Data Analysis and the Data Science Process, 10 periods) carry 22% each — the highest weightage in the option. Unit 4 carries 20%, and Units 2 and 5 carry 16% and 20%.
Which books should I refer to for Program Elective-4?
The syllabus lists 11 books, including Deepak Khemani. A First Course in Artificial Intelligence, McGraw Hill Education (India). and https://nptel.ac.in/courses/106106126/, among others. The full list is in the Complete Syllabus section above.
How can I download the Program Elective-4 syllabus PDF?
The PDF is free to download — use the download link in the Download section below. It is prepared by KAMALDNP Academy from the official BTEUP syllabus.
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Download the Program Elective-4 Syllabus PDF
Download the complete Program Elective-4 syllabus PDF below — prepared by KAMALDNP Academy, with the full topic list and reference books.
Download the Program Elective-4 Syllabus PDF
Source: BTEUP 3rd Year curriculum (folder2627), approved in C.D.C. meeting held on 16.07.2026. Topic lists follow the official syllabus; short explanations are the author's own plain-English summaries, not syllabus text. Please confirm with your own polytechnic for any local changes.
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