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Advance Machine Learning
Large Language Models (LLMs) and Foundation Models have transformed machine learning, especially in natural language processing, vision, and code generation. This chapter explores their architectures, training methods, applications, and ethical implications. Emphasizing the role of transformer architecture, it highlights both the potential and the challenges these models introduce in AI applications.
15 Chapters
16 weeks
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Course Chapters
Chapter 1
Learning Theory & Generalization
Chapter 2
Optimization Methods
Chapter 3
Kernel & Non-Parametric Methods
Chapter 4
Graphical Models & Probabilistic Inference
Chapter 5
Latent Variable & Mixture Models
Chapter 6
Ensemble & Boosting Methods
Chapter 7
Deep Learning & Neural Networks
Chapter 8
Non-Parametric Bayesian Methods
Chapter 9
Reinforcement Learning and Bandits
Chapter 10
Causality & Domain Adaptation
Chapter 11
Representation Learning & Structured Prediction
Chapter 12
Scalability & Systems
Chapter 13
Privacy-Aware and Robust Machine Learning
Chapter 14
Meta-Learning & AutoML
Chapter 15