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Lecture
Introduction to ML for Behavioral Data
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Related lectures (53)
Project Configuration Workshop
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Covers the configuration of a project management tool and defining project structures, geographical locations, and risks.
Machine Learning Basics
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Covers the basics of machine learning, including supervised and unsupervised techniques, linear regression, and model training.
Introduction to Machine Learning: Course Overview and Basics
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Introduces the course structure and fundamental concepts of machine learning, including supervised learning and linear regression.
Clustering & Density Estimation
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Covers clustering, PCA, LDA, K-means, GMM, KDE, and Mean Shift algorithms for density estimation and clustering.
Machine Learning: Supervised and Unsupervised Learning Techniques
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Covers supervised and unsupervised learning techniques in machine learning, highlighting their applications in finance and environmental analysis.
Linear Regression: Basics and Gradient Descent
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Covers the basics of linear regression, including feature engineering, supervised vs. unsupervised learning, and minimizing the cost function.
Regression Trees and Ensemble Methods in Machine Learning
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Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.
Entrepreneurship: Launching New Ventures
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Covers entrepreneurship, from identifying opportunities to profiting from innovation, emphasizing the mindset and practical aspects of starting ventures.
Image Classification: Decision Trees & Random Forests
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Explores image classification using decision trees and random forests to reduce variance and improve model robustness.
Introduction to Image Classification
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Covers image classification, clustering, and machine learning techniques like dimensionality reduction and reinforcement learning.
Untitled
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Introduction to Machine Learning: Basics and Examples
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Introduces the basics of machine learning, covering supervised learning, reinforcement learning, and dimension reduction.
Deep Generative Models
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Covers deep generative models, including variational autoencoders, GANs, and deep convolutional GANs.
Clustering & Density Estimation
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Covers dimensionality reduction, PCA, clustering techniques, and density estimation methods.
Untitled
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Optimization of Paper Planes
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Explores the optimization of paper planes and soft structures for thrust generation.
Boltzmann Machine
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Covers the Boltzmann Machine, a type of stochastic recurrent neural network.
Information Extraction: Approaches and Techniques
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Covers Information Extraction approaches, including hand-written patterns and supervised learning.
Unsupervised Learning: Clustering and Dimension Reduction
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Covers unsupervised learning, clustering, and dimension reduction techniques.
Optimal Errors and Phase Transitions
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Explores optimal errors and phase transitions in high dimensional models.
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