Course Objectives:
- Explore and comprehend advanced ML algorithms, their strengths, and weaknesses.
- Master techniques to interpret and explain ML model predictions for transparency and trust.
- Build and train deep learning models to address specific tasks and datasets.
- Apply the acquired knowledge to tackle real-world challenges in AI and ML domains.
Course Outcomes:
- CO1 Analyze and apply advanced machine learning algorithms to solve complex real-world problems.
- CO2 Evaluate and interpret ML models to understand their decision-making processes.
- CO3 Implement deep learning architectures for tasks like image analysis, language processing, and sequence modeling.
- CO4 Develop expertise in applying cutting-edge ML techniques to various AI applications and domains.
Unit I
Advanced ML Algorithms: Ensemble Learning and Ensemble methods: Bagging, Boosting, and Stacking, and Kernel Methods.
Reinforcement Learning: Q Learning, HMM model, Deep Reinforcement Learning
Unit II
Model Interpretability and Explainability: Feature importance and SHAP values, LIME (Local
Interpretable Model-agnostic Explanations), Explainable AI (XAI) techniques,
Unit III
Deep Learning Architectures: Convolutional Neural Networks (CNN) for image analysis,
Recurrent Neural Networks (RNN) for sequence data, Transformers and Attention mechanisms
Unit IV
Applications of Advanced ML: Natural Language Processing (NLP) with BERT and GPT,
Generative Adversarial Networks (GANs) for image synthesis, Transfer learning and domain adaptation
Textbooks:
- "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron
- "Pattern Recognition and Machine Learning" by Christopher M. Bishop
- "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
Reference Books:
- "Interpretable Machine Learning" by Christoph Molnar
- "Reinforcement Learning: An Introduction" by Richard S. Sutton and Andrew G. Barto
- "Natural Language Processing in Action" by Lane, Howard, and Hapke
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