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Non Conceptual Knowledge Systems
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Related lectures (52)
Supervised Learning: Classification Algorithms
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Explores supervised learning in financial econometrics, emphasizing classification algorithms like Naive Bayes and Logistic Regression.
Deep Generative Models
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Covers deep generative models, including variational autoencoders, GANs, and deep convolutional GANs.
Deep Learning: Exploring Vision and Language Transformers
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Covers advanced transformer architectures in deep learning, focusing on Swin, HUBERT, and Flamingo models for multimodal applications.
Financial Time Series Analysis
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Covers stylized facts of asset returns, summary statistics, testing for normality, Q-Q plots, and efficient market hypothesis.
Neural Network Approximation and Learning
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Delves into neural network approximation, supervised learning, challenges in high-dimensional learning, and deep learning experimental revolution.
Improving Models of the Ventral Visual Pathway
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Explores computational models of the ventral visual system, focusing on optimizing networks for real-world tasks and comparing to brain data.
Introduction to Image Classification
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Covers image classification, clustering, and machine learning techniques like dimensionality reduction and reinforcement learning.
Generative Adversarial Networks: Data Synthesis Techniques
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Discusses Generative Adversarial Networks and their applications in synthesizing data and generating images.
Chemical Reactions: Transformer Architecture
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Explores atom mapping in chemical reactions and the transition to reaction grammar using the transformer architecture.
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.
Unsupervised Learning: Clustering and Dimension Reduction
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Covers unsupervised learning, clustering, and dimension reduction techniques.
Data-Driven Modeling in Neuroscience: Meenakshi Khosla
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By Meenakshi Khosla explores data-driven modeling in large-scale naturalistic neuroscience, focusing on brain activity representation and computational models.
Optimal Errors and Phase Transitions
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Explores optimal errors and phase transitions in high dimensional models.
Optimization of Paper Planes
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Explores the optimization of paper planes and soft structures for thrust generation.
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.
Model Evaluation
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Explores underfitting, overfitting, hyperparameters, bias-variance trade-off, and model evaluation in machine learning.
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.
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 Machine Learning: Basics and Examples
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Introduces the basics of machine learning, covering supervised learning, reinforcement learning, and dimension reduction.
Clustering & Density Estimation
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Covers dimensionality reduction, PCA, clustering techniques, and density estimation methods.
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