Your roadmap · आपका रोडमैप
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Admission & first meetingप्रवेश और पहली मुलाकातEnquire at your nearest NDSS centre, meet the teacher, fix the batch time and get your study material.
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Part 1 of the syllabusपाठ्यक्रम भाग 1Unit-wise coaching with notes and doubt classes.
Unit 1: Introduction to AIUnit 2: Problem Solving and SearchUnit 3: Knowledge Representation
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Part 2 of the syllabusपाठ्यक्रम भाग 2Unit-wise coaching with notes and doubt classes.
Unit 4: Machine Learning BasicsUnit 5: AI Applications and Ethics
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Unit tests & assignmentsयूनिट टेस्टRegular tests in the university / board pattern.
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Semester 5 exam (university / board)विश्वविद्यालय / बोर्ड परीक्षाPrevious papers and full revision before the exam.
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Online tests & resultऑनलाइन टेस्ट और रिज़ल्टTake the tests online; your marks and result show in your student login and at the centre.
Syllabus · पाठ्यक्रम
1 subject · 5 chapters · 32 topics
01Unit 1: Introduction to AI
6 topics+
- Definition and history of AI
- Turing test
- Narrow, general and super AI
- Applications of AI in daily life and industry
- Intelligent agents - structure and types
- Agent environments - PEAS description
02Unit 2: Problem Solving and Search
7 topics+
- State space representation
- Water jug and 8-puzzle problems
- Breadth First Search and Depth First Search
- Heuristic functions
- Best-first search and A* algorithm
- Hill climbing
- Game playing - minimax and alpha-beta pruning
03Unit 3: Knowledge Representation
6 topics+
- Knowledge and its types
- Propositional logic
- Predicate (first-order) logic
- Rule-based systems - forward and backward chaining
- Semantic networks and frames
- Reasoning under uncertainty - basic probability
04Unit 4: Machine Learning Basics
7 topics+
- What is machine learning
- Supervised, unsupervised and reinforcement learning
- Linear regression and classification
- Decision tree and k-nearest neighbour
- k-means clustering
- Training, testing, accuracy and overfitting
- Introduction to neural networks
05Unit 5: AI Applications and Ethics
6 topics+
- Expert systems - architecture and examples
- Natural Language Processing and chatbots
- Computer vision and image recognition
- Robotics
- Generative AI tools
- Ethical issues - bias, privacy, job impact
About this course
You will be able to
- Explain the meaning, history, types and applications of artificial intelligence
- Describe intelligent agents and their environments
- Solve problems using uninformed search (BFS, DFS) and informed search (best-first, A*)
- Represent knowledge using propositional logic, predicate logic, rules and semantic networks
- Explain supervised, unsupervised and reinforcement learning and simple ML algorithms
- Describe AI applications such as NLP, computer vision, expert systems and chatbots and their ethical issues
Who it is for
- Diploma Computer Science & Engineering students of Semester 5 (BTEUP)
- Diploma IT students and beginners who want an introduction to AI
What you need
- Basic programming knowledge and school-level mathematics (probability basics helpful)
Online tests & result
Practice and unit tests online; marks and result show in your student login and at the centre.
Student login →
Questions
Which syllabus is followed?
The BTEUP Diploma Computer Science & Engineering Semester 5 Artificial Intelligence syllabus. Where your college syllabus differs, the centre follows your own syllabus.
Is heavy mathematics needed?
No. Only basic algebra and probability are used; search and ML ideas are explained with simple examples.
Is any coding done?
Small Python examples are shown for search and ML, but the exam focus is theory and solved problems.
