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Different types of learning
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Related lectures (32)
Reinforcement Learning Concepts
Covers key concepts in reinforcement learning, neural networks, clustering, and unsupervised learning, emphasizing their applications and challenges.
Supervised Learning: Classification Algorithms
Explores supervised learning in financial econometrics, emphasizing classification algorithms like Naive Bayes and Logistic Regression.
Introduction to Machine Learning: Course Overview and Basics
Introduces the course structure and fundamental concepts of machine learning, including supervised learning and linear regression.
Introduction to Machine Learning
Introduces machine learning concepts, from basics to advanced neural networks.
Interactive Lecture: Reinforcement Learning
Explores advanced reinforcement learning topics, including policies, value functions, Bellman recursion, and on-policy TD control.
Introduction to Machine Learning: Basics and Examples
Introduces the basics of machine learning, covering supervised learning, reinforcement learning, and dimension reduction.
Overfitting in Supervised Learning: Case Studies and Techniques
Addresses overfitting in supervised learning through polynomial regression case studies and model selection techniques.
Gradient Descent and Linear Regression
Covers stochastic gradient descent, linear regression, regularization, supervised learning, and the iterative nature of gradient descent.
Supervised Learning: Regression Methods
Explores supervised learning with a focus on regression methods, including model fitting, regularization, model selection, and performance evaluation.
Machine Learning: Types and Applications
Covers the types of machine learning, including supervised, unsupervised, and reinforcement learning.
Machine Learning Fundamentals
Covers the fundamental concepts of machine learning, including classification, algorithms, optimization, supervised learning, reinforcement learning, and various tasks like image recognition and text generation.
Gradient-Based Algorithms in High-Dimensional Learning
Provides insights on gradient-based algorithms, deep learning mysteries, and the challenges of non-convex problems.
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