Skip to main content
Graph
Search
fr
en
Login
Search
All
Categories
Concepts
Courses
Lectures
MOOCs
People
Quizes
Exercises
Publications
Startups
Units
Show all results for
Home
Lecture
Introduction to Supervised Learning and Decision Theory
Graph Chatbot
Related lectures (51)
Decision Theory: Risk and Inference
Explores decision theory, risk functions, and inference in statistical analysis.
Decision Theory: Risk and Hypothesis Testing
Covers decision theory, risk functions, and hypothesis testing in statistical inference.
Discriminant Analysis: Bayes Rule
Covers the Bayes discriminant rule for allocating individuals to populations based on measurements and prior probabilities.
Supervised Learning: Linear Regression
Covers supervised learning with a focus on linear regression, including topics like digit classification, spam detection, and wind speed prediction.
Examples of decision models for Supervised learning
Covers examples of decision models for supervised learning, including regression, classification, ranking pairs, and sequence decoding for OCR models.
Maximum Likelihood Estimation
Covers Maximum Likelihood Estimation, focusing on ML Estimation-Distribution, Shrinkage Estimation, and Loss functions.
Gradient Descent and Linear Regression
Covers stochastic gradient descent, linear regression, regularization, supervised learning, and the iterative nature of gradient descent.
Classification Detection
Covers binary hypothesis testing and decision functions in specific scenarios.
Introduction to Machine Learning: Supervised Learning
Introduces supervised learning, covering classification, regression, model optimization, overfitting, and kernel methods.
Supervised Learning Overview
Covers CNNs, RNNs, SVMs, and supervised learning methods, emphasizing the importance of tuning regularization and making informed decisions in machine learning.
Machine Learning Biases
Covers the basics of machine learning, challenges in deployment, adversarial attacks, and privacy concerns.
Nonlinear Supervised Learning
Explores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.
Statistical Learning: Fundamentals
Log in to Mediaspace to watch this video
Introduces the fundamentals of statistical learning, covering supervised learning, decision theory, risk minimization, and overfitting.
Supervised Learning: Formalization and Cost Functions
Log in to Mediaspace to watch this video
Covers the formalism for supervised learning and decision functions in classification problems.
Statistical Theory: Cramér-Rao Bound & Hypothesis Testing
Log in to Mediaspace to watch this video
Explores the Cramér-Rao bound, hypothesis testing, and optimality in statistical theory.
Supervised Learning: Classification Algorithms
Log in to Mediaspace to watch this video
Explores supervised learning in financial econometrics, emphasizing classification algorithms like Naive Bayes and Logistic Regression.
Image Processing II: Bayesian Classification and Decision Making
Log in to Mediaspace to watch this video
Explores Bayesian classification, decision making, and pattern recognition applications in image processing.
Supervised Learning: Regression Methods
Log in to Mediaspace to watch this video
Explores supervised learning with a focus on regression methods, including model fitting, regularization, model selection, and performance evaluation.
Decision Trees: Classification
Log in to Mediaspace to watch this video
Explores decision trees for classification, entropy, information gain, one-hot encoding, hyperparameter optimization, and random forests.
Supervised Learning in Asset Pricing
Log in to Mediaspace to watch this video
Explores supervised learning in asset pricing, focusing on stock return prediction challenges and model assessment.
Previous
Page 1 of 3
Next
