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Machine Learning Course Outline Chandigarh:
- Introduction to Machine Learning:
- Definition and basic concepts
- Types of machine learning: supervised learning, unsupervised learning, reinforcement learning
- Applications of machine learning
- Linear Algebra and Probability:
- Linear algebra basics (vectors, matrices, eigenvalues)
- Probability and statistics for machine learning
- Supervised Learning:
- Linear regression
- Logistic regression
- Support Vector Machines (SVM)
- Decision Trees and Random Forests
- Neural Networks and Deep Learning
- Unsupervised Learning:
- Clustering techniques (K-means, hierarchical clustering)
- Dimensionality reduction (Principal Component Analysis – PCA)
- Association rule learning
- Model Evaluation and Validation:
- Cross-validation
- Bias-variance tradeoff
- Evaluation metrics (precision, recall, F1-score, ROC curves)
- Ensemble Methods:
- Bagging and boosting
- Random Forests
- Gradient Boosting
- Neural Networks and Deep Learning:
- Feedforward neural networks
- Backpropagation
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN)
- Transfer learning
- Natural Language Processing (NLP) and Computer Vision:
- Text processing and sentiment analysis
- Image processing and object recognition
- Reinforcement Learning:
- Markov Decision Processes
- Q-learning
- Deep Reinforcement Learning
- Special Topics:
- Explainable AI
- Generative Adversarial Networks (GANs)
- Autoencoders
- Practical Applications and Case Studies:
- Real-world applications of machine learning in various domains
- Hands-on projects and assignments
- Ethical and Social Implications:
- Bias and fairness in machine learning
- Ethical considerations in AI
