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AKTU Artificial Intelligence & Machine Learning (BMI501) Syllabus PDF – Integrated MCA 5th Semester
Artificial Intelligence & Machine Learning BMI501
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Official AKTU syllabus of Artificial Intelligence & Machine Learning (BMI501) for Integrated MCA 5th Semester – 3 credits, L-T-P 3-0-0. Read the unit-wise course content, course outcomes, evaluation pattern and recommended books online, or download the PDF.
Syllabus — Artificial Intelligence & Machine Learning
Download PDFBMI501 · 3 credits · L-T-P 3-0-0
Course Outcomes
At the end of course , the student will be able to understand
| CO1 | Explain basic concepts, foundation areas, historical development, applications of Artificial intelligence and intelligent agents. | K 2 |
| CO2 | Apply uniformed, informed and local search techniques for solving AI problems | K 3 |
| CO3 | Understand, apply and analyze knowledge representation and reasoning techniques to create knowledge base of an intelligent machine. | K 2 |
| CO4 | Identify fundamental concepts, types, issues and application areas of machine learning and data preprocessing. | K 1 |
| CO5 | Apply and evaluate machine learning algorithms to solving real-world problems Introduction, types, components of learning process, issues, application areas and performance measurement criteria of learning algorithm. | K 3 |
Course Content
| Unit | Topic | Proposed Lecture |
|---|---|---|
| I | Artificial Intelligence: Introduction, historical development, foundation areas, tasks and application areas. Intelligent Agents: Introduction, types and structure of intelligent agents. Computer Vision, Natural language processing. | 08 |
| II | Searching Techniques: Introduction, Problem solving by searching, Searching for solutions, Uniformed and Informed searching techniques. Local search algorithms, Adversarial search methods, Search techniques used in games, Alpha-Beta pruning. | 08 |
| III | Knowledge Representation and Reasoning: Propositional logic, Predicate logic, First order logic, Inference in first order logic, Clause form conversion, Resolution. Chaining: Concept, forward chaining and backward chaining, Utility theory and Probabilistic reasoning. | 08 |
| IV | Machine Learning: Introduction, types, components of learning process, issues, application areas and performance measurement criteria of learning algorithm, Data preprocessing, Data transformation, Scaling training and test data. Supervised Learning: Introduction, operations, types of learning algorithms, Regression, Classification algorithms – Decision tree, Support vector machine, K-Nearest Neighbors, Naïve Bayes. | 08 |
| V | Unsupervised learning: Introduction, types, working, issues, application areas, clustering algorithms (K-means, Hierarchical), Association rule learning and dimensionality reduction. Reinforcement learning: Introduction, components, types, working, issues and application areas. | 08 |
Suggested Readings
- Russell S. and Norvig P., “Artificial Intelligence – A Modern Approach”, Pearson Education. 2. Rich E. and Knight K., “Artificial Intelligence”, McGraw Hill Publications.
- Charnik E. and McDermott D., “Introduction to Artificial Intelligence”, Pearson Education. 4. Patterson D. W., “Artificial Intelligence and Expert Systems”, PHI Publications.
- Khemani D., “A First Course in Artificial Intelligence”, McGraw Hill.
- Winston P. H., “Artificial Intelligence”, Pearson Education.
- Mitchell T.M., “Machine Learning”, McGraw Hill.
- Alpaydin E. “Introduction to Machine Learning”, MIT Press.
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