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AKTU Artificial Intelligence and Machine Learning in Bioinformatics (BBI602) Syllabus PDF – B.Tech 3rd Year
Artificial Intelligence and Machine Learning in Bioinformatics BBI602
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Official AKTU syllabus of Artificial Intelligence and Machine Learning in Bioinformatics (BBI602) for B.Tech 3rd Year – 4 credits, L-T-P 3-1-0. 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
Download PDFBBI602 · 4 credits · L-T-P 3-1-0
Course Outcomes
At the end of the course, the student will be able to:
| CO1 | Explain the fundamental concepts of Artificial Intelligence. | K2 |
| CO2 | Describe modern AI technologies and their applications in intelligent systems and healthcare. | K2 |
| CO3 | Apply the fundamental concepts and workflow of machine learning for data analysis and predictive modeling. | K3 |
| CO4 | Apply supervised and unsupervised machine learning algorithms to solve classification, regression and clustering problems. | K3 |
| CO5 | Apply AI and machine learning approaches to solve bioinformatics and healthcare problems. | K3 |
Course Content
L-T-P: 3-1-0
| Unit | Topic | Proposed Lecture |
|---|---|---|
| I | FUNDAMENTALS OF ARTIFICIAL INTELLIGENCE: Introduction to Artificial Intelligence, history and evolution of AI, types of AI (Narrow, General and Super AI), intelligent agents, problem-solving approaches, knowledge representation and reasoning, search techniques (Breadth First Search, Depth First Search and A* Search). | 08 |
| II | MODERN ARTIFICIAL INTELLIGENCE: Expert systems, Natural Language Processing (NLP), computer vision, robotics, introduction to Generative AI and Large Language Models (LLMs), AI ethics, bias, privacy and security, and emerging trends in Artificial Intelligence. | 08 |
| III | FUNDAMENTALS OF MACHINE LEARNING: Introduction to Machine Learning, Artificial Intelligence vs Machine Learning vs Deep Learning, machine learning workflow, types of machine learning (Supervised Learning, Unsupervised Learning and Reinforcement Learning), data collection and pre-processing, feature selection, training, validation and testing datasets, overfitting and under fitting, and model evaluation using accuracy, precision, recall, F1-score and confusion matrix. | 08 |
| IV | SUPERVISED AND UNSUPERVISED LEARNING ALGORITHMS: Supervised learning algorithms: Linear Regression, Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbour (KNN) and Naïve Bayes. Unsupervised learning algorithms: K-Means Clustering, Hierarchical Clustering and Principal Component Analysis (PCA). Introduction to Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN). | 08 |
| V | APPLICATIONS OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN BIOINFORMATICS: Applications of Artificial Intelligence and Machine Learning in genomics, proteomics, transcriptomics, biological sequence analysis, disease diagnosis, drug discovery, protein structure prediction, precision medicine, medical image analysis, personalized medicine, AI-assisted clinical decision support systems, and ethical considerations in AI-based healthcare. | 08 |
Text Books
- Stuart Russell, Peter Norvig, “Artificial Intelligence: A Modern Approach”, 4th Edition, Pearson Education, 2021.
- Aurélien Géron, “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow”, 3rd Edition, O'Reilly Media, 2022.
- Arthur Lesk, “Introduction to Bioinformatics”, 5th Edition, Oxford University Press, 2019.
- David W. Mount, “Bioinformatics: Sequence and Genome Analysis”, 2nd Edition, Cold Spring Harbor Laboratory Press, 2004.
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