AL3411 AI & ML Lab Syllabus
AL3411 ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING LABORATORY
| Exp. No. | List of Experiments | Supporting Methods/Context |
| 1(A) | Implementation of Uninformed Search Algorithms (BFS) | Includes implementation procedures using Python. |
| 1(B) | Implementation of Uninformed Search Algorithms (DFS) | Implementation involves using Python and managing stack operations,. |
| 2(A) | Implementation of Informed Search Algorithms (A*) | A* algorithm aims to select the node with the smallest value of the evaluation function $(g+h)$,. |
| 2(B) | Implementation of Informed Search Algorithms (Memory-Bounded AO* Search) | AO* proceeds like A*, expanding the best leaf until memory is full, and cannot add a new node without dropping the worst one,. |
| 3 | Implement Naïve Bayes Models | Implementation procedures include importing libraries, data preprocessing, training, testing, and visualizing the model. |
| 4 | Implement Bayesian Networks | Program to find Bayesian networks, using estimators like Maximum Likelihood Estimator,,. |
| 5(A) | Build Regression Models (Linear Regression) | Procedure includes data preprocessing, fitting the Multiple Linear Regression model to the training set, and predicting results. |
| 5(B) | Build Regression Models (Logistics Regression Model) | Procedure involves data preprocessing, extracting independent/dependent variables, splitting data, feature scaling, fitting the model, and predicting results,. |
| 6(A) | Build Decision Trees | Decision tree models are built using a Gaussian classifier, often incorporating the ID3 algorithm,. |
| 6(B) | Build Random Forest Tree | Random forest tree models are built using a Gaussian classifier,. |
| 7 | Build SVM Models | Builds Support Vector Machine models, procedures include data preprocessing, creating a SVM Classifier, training, prediction, normalization, and calculating ROC curves,,,. |
| 8 | Implement Ensembling Techniques | Illustrated by implementing K-Means clustering,. |
| 9 | Implement Clustering Algorithms | Implementation using the K-Means method involves data preprocessing, training the model, and visualizing the clusters. |
| 10 | Implement EM for Bayesian Networks | Implementation of the Expectation-Maximization (EM) algorithm for Bayesian Networks (e.g., using the two-coin problem),. |
| 11 | Build Simple Neural Network Models | Involves importing data (like MNIST dataset), designing a sequential model with dense layers and activation functions (e.g., sigmoid/softmax), compiling, training, and testing,,. |
| 12 | Build Deep Learning Neural Network Models | Primarily focuses on Convolutional Neural Networks (CNNs) for large-size input data such as images,,. |
Course Details and Outcomes
The course is designated by the code AL3411 and titled ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING LABORATORY,. The credit distribution is L T P C: 0 0 4 2, covering a TOTAL: 60 PERIODS.
Tools and Data Sets:
Tools: Python, Numpy, Scipy, Matplotlib, Pandas, statmodels, seaborn, plotly, bokeh.
Example Data Sets: UCI, Iris, Pima Indians Diabetes etc..
Course Objectives:
The objectives of the course are to learn to:
Learn uninformed and informed search techniques.
Build a knowledge base in Prolog and process queries to perform inference.
Build supervised learning models.
Explore the regression models.
To learn, develop, and evaluate the performance of different models.
Outcomes (COs):
Upon completion of the course, students will be able to:
CO1: Implement uninformed and informed search techniques.
CO2: Build a knowledge base in Prolog and process queries to perform inference.
CO3: Develop supervised learning models.
CO4: Develop regression models.