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You can keep up to 10 CVs here -- delete one you don't need.
You can keep up to 10 CVs here -- delete one you don't need.
You can keep up to 10 CVs here -- delete one you don't need.
You can keep up to 10 CVs here -- delete one you don't need.
You can keep up to 10 CVs here -- delete one you don't need.

Artificial Intelligence (Diploma)

Search techniques, knowledge representation, machine learning basics and AI applications.

6 MonthsDuration 5Chapters32Topics
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Syllabus · पाठ्यक्रम

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01Unit 1: Introduction to AI 6 topics
  1. Definition and history of AI
  2. Turing test
  3. Narrow, general and super AI
  4. Applications of AI in daily life and industry
  5. Intelligent agents - structure and types
  6. Agent environments - PEAS description
02Unit 2: Problem Solving and Search 7 topics
  1. State space representation
  2. Water jug and 8-puzzle problems
  3. Breadth First Search and Depth First Search
  4. Heuristic functions
  5. Best-first search and A* algorithm
  6. Hill climbing
  7. Game playing - minimax and alpha-beta pruning
03Unit 3: Knowledge Representation 6 topics
  1. Knowledge and its types
  2. Propositional logic
  3. Predicate (first-order) logic
  4. Rule-based systems - forward and backward chaining
  5. Semantic networks and frames
  6. Reasoning under uncertainty - basic probability
04Unit 4: Machine Learning Basics 7 topics
  1. What is machine learning
  2. Supervised, unsupervised and reinforcement learning
  3. Linear regression and classification
  4. Decision tree and k-nearest neighbour
  5. k-means clustering
  6. Training, testing, accuracy and overfitting
  7. Introduction to neural networks
05Unit 5: AI Applications and Ethics 6 topics
  1. Expert systems - architecture and examples
  2. Natural Language Processing and chatbots
  3. Computer vision and image recognition
  4. Robotics
  5. Generative AI tools
  6. Ethical issues - bias, privacy, job impact

About

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)

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.

Download the syllabus (PDF)

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