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AKTU Artificial Intelligence and Machine Learning in Bioinformatics Lab (BBI652) Syllabus PDF – B.Tech 3rd Year
Artificial Intelligence and Machine Learning in Bioinformatics Lab BBI652
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- Semester 6
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Official AKTU syllabus of Artificial Intelligence and Machine Learning in Bioinformatics Lab (BBI652) for B.Tech 3rd Year – 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 and Machine Learning in Bioinformatics Lab
Download PDFBBI652 · 1 credits · L-T-P 0-0-2
Course Outcomes
At the end of the course, the student will be able to:
| CO1 | Demonstrate the use of Python and AI tools for data analysis. | K2 |
| CO2 | Perform data pre-processing and visualization using machine learning libraries. | K3 |
| CO3 | Apply supervised machine learning algorithms to biological datasets. | K3 |
| CO4 | Apply unsupervised machine learning techniques for data clustering and analysis. | K3 |
| CO5 | Apply evaluation metrics to interpret machine learning results for bioinformatics applications. | K3 |
List of Experiments
- Introduction to Python programming and Jupyter Notebook/Google Colab.
- Study of Python libraries: NumPy, Pandas, Matplotlib, and Scikit-learn.
- Loading, exploring, and visualizing biological datasets using Pandas and Matplotlib.
- Data pre-processing: handling missing values, feature scaling, and splitting data into training and testing sets.
- Implementation of Linear Regression and Logistic Regression using Scikit-learn.
- Implementation of Decision Tree and Random Forest for classification.
- Implementation of K-Nearest Neighbour (KNN) and Support Vector Machine (SVM) for classification.
- Implementation of K-Means Clustering and visualization of clustered data.
- Model performance evaluation using Accuracy, Precision, Recall, F1-score, and Confusion Matrix.
- Case study: Disease prediction using a healthcare dataset.
- Case study: Gene expression or DNA sequence classification using a publicly available bioinformatics dataset. Note: The instructor may add / delete / modify / tune experiments as required.
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