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AKTU Artificial Intelligence & Machine Learning Lab (BMI551) Syllabus PDF – Integrated MCA 5th Semester
Artificial Intelligence & Machine Learning Lab BMI551
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Official AKTU syllabus of Artificial Intelligence & Machine Learning Lab (BMI551) for Integrated MCA 5th Semester – 1 credits, L-T-P 0-0-2. Read the unit-wise course content, course outcomes, evaluation pattern and recommended books online, or download the PDF.
Syllabus — Artificial Intelligence & Machine Learning Lab
Download PDFBMI551 · 1 credits · L-T-P 0-0-2
Course Outcomes
At the end of course, the student will be able to
| CO1 | Demonstrate the working of different types of intelligent agents and apply AI problem solving techniques to solve problems | K 2 |
| CO2 | Implement and analyze uninformed, informed and local search techniques for AI problem solving. | K 3 |
| CO3 | Develop programs for knowledge representation and reasoning. | K 3 |
| CO4 | Implement and analyze various supervised, unsupervised and reinforcement machine learning algorithms. | K 3 |
| CO5 | Implement and evaluate data preprocessing and data transformation techniques | K 3 |
Course Content
- Program to demonstrate different types of intelligent agents.
- Program to solve AI problems (4-3 water jug, 8 -puzzle, 4 queens, 8 queens).
- Program to implement uninformed search algorithm (Breadth-first search, Depth-first search, Bidirectional, Uniform Cost).
- Program to implement heuristic search algorithm (Hill climbing, Best-first search, A*, AO*).
- Program to implement different game playing techniques.
- Program to implement various knowledge representation techniques.
- Program to implement chaining algorithm (forward, backward).
- Program to implement Regression (linear, multi-linear, logistic).
- Program to implement Classification algorithm (Decision tree, support vector machine, K-nearest neighbor, Naïve Bayes)
- Program to implement clustering algorithm (k-means, Hierarchical)
- Program to implement Association rule learning techniques.
- Program to implement algorithm for Reinforcement learning.
- Program to pre-process a real-world data set.
- Program to import and explore a dataset and identify the missing / duplicate values.
- Program to split a dataset into training and testing datasets. Note: The Instructor may add/delete/modify experiments, wherever he/she feels in a justified manner. MCA (Integrated) THIRD YEAR
Course Content
SEMESTER-VI
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