List of Experiments / Activities
- 1Program to demonstrate different types of intelligent agents.
- 2Program to solve AI problems (4-3 water jug, 8 -puzzle, 4 queens, 8 queens).
- 3Program to implement uninformed search algorithm (Breadth-first search, Depth-first search, Bidirectional, Uniform Cost).
- 4Program to implement heuristic search algorithm (Hill climbing, Best-first search, A*, AO*).
- 5Program to implement different game playing techniques.
- 6Program to implement various knowledge representation techniques.
- 7Program to implement chaining algorithm (forward, backward).
- 8Program to implement Regression (linear, multi-linear, logistic).
- 9Program to implement Classification algorithm (Decision tree, support vector machine, K-nearest neighbor, Naïve Bayes)
- 10Program to implement clustering algorithm (k-means, Hierarchical)
- 11Program to implement Association rule learning techniques.
- 12Program to implement algorithm for Reinforcement learning.
- 13Program to pre-process a real-world data set.
- 14Program to import and explore a dataset and identify the missing / duplicate values.
- 15Program 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.
Course Outcomes (COs)
After completion of the course student will be able to:
- CO1Demonstrate the working of different types of intelligent agents and apply AI problem solving techniques to solve problemsK2
- CO2Implement and analyze uninformed, informed and local search techniques for AI problem solving.K3
- CO3Develop programs for knowledge representation and reasoning.K3
- CO4Implement and analyze various supervised, unsupervised and reinforcement machine learning algorithms.K3
- CO5Implement and evaluate data preprocessing and data transformation techniquesK3
Official AKTU syllabus
This subject is on page 21 of the original AKTU PDF.