Artificial Intelligence in Healthcare
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Follow on Instagram for UpdatesArtificial Intelligence in Healthcare Syllabus — Unit-wise Content
The official GTU curriculum for Artificial Intelligence in Healthcare (subject code ME02031041) as prescribed for BE · Semester 2 divides the course into 5 units, covering 49 listed topics. The unit-wise breakdown below is read from the GTU syllabus document for this subject code, so you can check what the course actually covers before deciding which papers to attempt.
Download the official syllabus PDF (ME02031041)Unit 1 Introduction to Artificial Intelligence
- Overview of AI, Goals of AI, Advantages of AI Systems
- Challenges of AI Systems
- Sub-domains of AI, Types of AI, Application of AI, Overview of ML, Application of ML, Key Elements of ML, Types of Learning - Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
Unit 2 Preparing to Model
- Basic data types
- exploring structure of Data – Numerical Data
- Categorical Data
- Exploring relationship between variables
- Data Quality and Remediation
- Data Preprocessing Modelling and Evaluation: Selecting a Model
- Training Model – Holdout
- K fold cross validation
- bootstrap sampling
- model representation and interpretability – Under fitting
- over fitting
- bias variance tradeoff
- Model Performance Evaluation – classification
- performance improvement Feature Engineering – Feature construction
- Extraction and Selection
Unit 3 Supervised Learning Algorithms – Classification & Regression –
- Steps of Classification Learning Classification Algorithm - k- Nearest Neighbour (kNN) – Working
- Decision tree - Working
- Random Forest Model - Working
- Support Vector Machines - Working
- Application Regression Algorithm - Linear Regression
- Polynomial Regression
- Logistic Regression
- Case Study
Unit 4 Unsupervised Learning Algorithms – K - means for Clustering
- Apriori Algorithm
- Principal Component Analysis (PCA)
- Singular Value Decomposition (SVD)
- Independent Component Analysis (ICA)
- Case Study
Unit 5 Introduction to Deep Leaning
- Relation between AI, ML, DL, ML Vs
- DL, Working of Deep Learning
- Applications of Deep Learning
- Activation Functions Convolutional Neural Networks (CNN) - Working
- CNN Layers
- Building CNN Model
- Evaluate and Predication using the Model
- Case study Program Name: Master of Engineering Level: PG Branch: Biomedical Engineering Subject Code : ME02031041 Subject Name: Artificial Intelligence in Healthcare Recurrent Neural Network (RNN) – Working of LSTM
- Train and Evaluating Model
- Saving and Loading Model
- Time Series data Analysis using RNN – Case Study
- Computer Vision – Installing of OpenCV
- Reading of Image
- Resizing of Image
- Face detection
- Object detection using YOLO Algorithm
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