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Related lectures (52)

Linear Regression: Basics Log in to Mediaspace to watch this video

Covers the basics of linear regression, binary and multi-class classification, and evaluation metrics.

Machine Learning Basics Log in to Mediaspace to watch this video

Covers the basics of machine learning, including supervised and unsupervised techniques, linear regression, and model training.

Deep Learning: Data Representations and Neural Networks Log in to Mediaspace to watch this video

Covers data representations, Bag of Words, histograms, data pre-processing, and neural networks.

Supervised Learning Fundamentals Log in to Mediaspace to watch this video

Introduces the fundamentals of supervised learning, including loss functions and probability distributions.

Document Analysis: Topic Modeling Log in to Mediaspace to watch this video

Explores document analysis, topic modeling, and generative models for data generation in machine learning.

Deep Learning: Data, Models, and Challenges Log in to Mediaspace to watch this video

Provides an overview of deep learning concepts, focusing on data, model architecture, and challenges in handling large datasets.

Data-Driven Modeling: Regression Log in to Mediaspace to watch this video

Introduces data-driven modeling with a focus on regression, covering linear regression, risks of inductive reasoning, PCA, and ridge regression.

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Linear Regression: Basics and Estimation Log in to Mediaspace to watch this video

Covers the basics of linear regression and how to solve estimation problems using least squares and matrix notation.

Regression: High Dimensions Log in to Mediaspace to watch this video

Explores linear regression in high dimensions and practical house price prediction from a dataset.

Feature Engineering: Polynomial Regression Log in to Mediaspace to watch this video

Covers fitting linear regression on features of the original predictors for flexible feature representation.

Secure Collaborative Systems: Building Efficient Protocols for Linear Models Log in to Mediaspace to watch this video

Explores building efficient protocols for linear models in secure collaborative systems, emphasizing security guarantees and computational efficiency.

Linear Regression: Model Adjustment and Parameter Estimation Log in to Mediaspace to watch this video

Explains the decomposition of total sum of squares, model adjustment, and parameter estimation in linear regression.

Supervised Learning in Financial Econometrics Log in to Mediaspace to watch this video

Explores supervised learning in financial econometrics, covering linear regression, model fitting, potential problems, basis functions, subset selection, cross-validation, regularization, and random forests.

Basics of linear regression model Log in to Mediaspace to watch this video

Covers the basics of linear regression, OLS method, predicted values, residuals, matrix notation, goodness-of-fit, hypothesis testing, and confidence intervals.

Cost Modelling of Materials Log in to Mediaspace to watch this video

Explores cost modelling of materials, focusing on technical approaches, automotive case studies, and sustainability, emphasizing the importance of data quality and the impact of weight and emissions on OEMs.

Machine Learning Fundamentals: Regularization and Cross-validation Log in to Mediaspace to watch this video

Explores overfitting, regularization, and cross-validation in machine learning, emphasizing the importance of feature expansion and kernel methods.

Data Science Visualization with Pandas Log in to Mediaspace to watch this video

Covers data manipulation and exploration using Python with a focus on visualization techniques.

Decision Trees and Random Forests: Concepts and Applications Log in to Mediaspace to watch this video

Discusses decision trees and random forests, focusing on their structure, optimization, and application in regression and classification tasks.

Statistics: Exploratory Data Analysis Log in to Mediaspace to watch this video

Introduces statistics basics, including data analysis and probability theory, emphasizing central tendency, dispersion, and distribution shapes.

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