Канал: MLVU

MLVU Course details 2024

MLVU Course details 2024

MLVU 6.2: Local and global derivatives

MLVU 6.2: Local and global derivatives

MLVU 6.3: Backpropagation

MLVU 6.3: Backpropagation

MLVU Course details 2023

MLVU Course details 2023

MLVU 1.4 Social impact 1

MLVU 1.4 Social impact 1

MLVU 1.5 Generalization in machine learning

MLVU 1.5 Generalization in machine learning

MLVU 1.3 Other abstract tasks: regression, clustering, density estimation

MLVU 1.3 Other abstract tasks: regression, clustering, density estimation

MLVU 1.2 Classification

MLVU 1.2 Classification

MLVU 13.5: Social impact 4

MLVU 13.5: Social impact 4

MLVU 13.4: AlphaGo

MLVU 13.4: AlphaGo

MLVU 13.3: (Deep) Q Learning

MLVU 13.3: (Deep) Q Learning

MLVU 13.2: Policy gradients and random search

MLVU 13.2: Policy gradients and random search

MLVU 13.1: Reinforcement learning

MLVU 13.1: Reinforcement learning

MLVU 12.5: Validation of embedding models

MLVU 12.5: Validation of embedding models

MLVU 12.4: Graph models

MLVU 12.4: Graph models

MLVU 12.3: PCA revisited

MLVU 12.3: PCA revisited

MLVU 12.2: Improving recommender systems

MLVU 12.2: Improving recommender systems

MLVU 12.1: Recommender systems

MLVU 12.1: Recommender systems

MLVU 11.3: Recurrent neural nets and LSTMs

MLVU 11.3: Recurrent neural nets and LSTMs

MLVU 11.2: Deep learning on sequences

MLVU 11.2: Deep learning on sequences

MLVU 11.1: Markov models

MLVU 11.1: Markov models

MLVU 10.4: Boosting: Adaboost and gradient boosting

MLVU 10.4: Boosting: Adaboost and gradient boosting

MLVU 10.3: Ensembling: stacking, bagging and random forests

MLVU 10.3: Ensembling: stacking, bagging and random forests

MLVU 10.2: Regression trees and decision trees with numeric features

MLVU 10.2: Regression trees and decision trees with numeric features

MLVU 10.1: Decision trees

MLVU 10.1: Decision trees

MLVU 9.4: Variational autoencoders (VAEs)

MLVU 9.4: Variational autoencoders (VAEs)

MLVU 9.3: Autoencoders

MLVU 9.3: Autoencoders

MLVU 9.2: Generative adversarial networks (GANs)

MLVU 9.2: Generative adversarial networks (GANs)

MLVU 9.1: Generator networks

MLVU 9.1: Generator networks

MLVU 8.5: Social impact 3

MLVU 8.5: Social impact 3

MLVU 8.4: Expectation-maximization from first principles

MLVU 8.4: Expectation-maximization from first principles

MLVU 8.3: Expectation-maximization

MLVU 8.3: Expectation-maximization

MLVU 8.2: Maximum likelihood estimators

MLVU 8.2: Maximum likelihood estimators

MLVU 8.1: Normal distributions

MLVU 8.1: Normal distributions

MLVU 7.4: Making it work

MLVU 7.4: Making it work

MLVU 7.3: Convolutions

MLVU 7.3: Convolutions

MLVU 7.2: Tensor backpropagation

MLVU 7.2: Tensor backpropagation

MLVU 7.1: Deep learning and automatic differentiation

MLVU 7.1: Deep learning and automatic differentiation

MLVU 6.5: The kernel trick

MLVU 6.5: The kernel trick

MLVU: Lagrange multipliers

MLVU: Lagrange multipliers

MLVU 6.4: Maximum margin loss

MLVU 6.4: Maximum margin loss

MLVU 6.1: Neural networks

MLVU 6.1: Neural networks

MLVU 5.5: Information theory

MLVU 5.5: Information theory

MLVU 5.4: Logistic regression

MLVU 5.4: Logistic regression

MLVU 5.3: The (naive) Bayes classfier

MLVU 5.3: The (naive) Bayes classfier

MLVU 5.2: Learning with probability

MLVU 5.2: Learning with probability

MLVU 5.1: Introduction to probability

MLVU 5.1: Introduction to probability

MLVU 4.4: Principal Component Analysis

MLVU 4.4: Principal Component Analysis

MLVU 3.2: Model evaluation

MLVU 3.2: Model evaluation

MLVU 4.3: Normalization

MLVU 4.3: Normalization

MLVU 4.2: Class imbalance and feature design

MLVU 4.2: Class imbalance and feature design

MLVU 4.1: Missing values and outliers

MLVU 4.1: Missing values and outliers

MLVU 3.6: No free lunch

MLVU 3.6: No free lunch

MLVU 3.5: Statistics for Machine Learning Experiments

MLVU 3.5: Statistics for Machine Learning Experiments

MLVU 3.4: Social Impact 2

MLVU 3.4: Social Impact 2

MLVU 3.3: ROC Curves, Area Under the Curve

MLVU 3.3: ROC Curves, Area Under the Curve

MLVU 3.1: Machine learning experiments

MLVU 3.1: Machine learning experiments

MLVU 2.4 Linear Classification

MLVU 2.4 Linear Classification

MLVU 2.3 Gradient descent

MLVU 2.3 Gradient descent

MLVU 2.2 Searching for a good model

MLVU 2.2 Searching for a good model

MLVU 2.1 Linear regression

MLVU 2.1 Linear regression

MLVU Live Stream

MLVU Live Stream

MLVU 1.1 What is machine learning?

MLVU 1.1 What is machine learning?

12 Recommendation, Embeddings and Matrix Factorization

12 Recommendation, Embeddings and Matrix Factorization

11 Sequences: Markov Models, Word2Vec and LSTMs (MLVU)

11 Sequences: Markov Models, Word2Vec and LSTMs (MLVU)

10 Tree Models and Ensembles: Decision Trees, AdaBoost, Gradient Boosting (MLVU2020)

10 Tree Models and Ensembles: Decision Trees, AdaBoost, Gradient Boosting (MLVU2020)

9 Deep Learning for Generative Models: GANs and VAEs (MLVU2020)

9 Deep Learning for Generative Models: GANs and VAEs (MLVU2020)

8 Probability 2: Density estimation. Maximum likelihood, Normal Distributions,

8 Probability 2: Density estimation. Maximum likelihood, Normal Distributions,

7 Deep Learning: tensor backpropagation, convolutional layers (MLVU2020)

7 Deep Learning: tensor backpropagation, convolutional layers (MLVU2020)

6 Linear Models 2: Neural Networks, Scalar Backpropagation, SVMs and Kernel methods (MLVU2020)

6 Linear Models 2: Neural Networks, Scalar Backpropagation, SVMs and Kernel methods (MLVU2020)

5 Probability 1: Logistic regression, Log loss, Entropy (MLVU2020)

5 Probability 1: Logistic regression, Log loss, Entropy (MLVU2020)

4 Methodology for pre-processing, PCA, Eigenfaces (MLVU2020)

4 Methodology for pre-processing, PCA, Eigenfaces (MLVU2020)

3 Methodology for evaluation: AUC, Bias and Variance (MLVU2020)

3 Methodology for evaluation: AUC, Bias and Variance (MLVU2020)

2 Linear Regression, Classification and Gradient Descent (MLVU2020)

2 Linear Regression, Classification and Gradient Descent (MLVU2020)

1 Introduction to Machine Learning (MLVU2020)

1 Introduction to Machine Learning (MLVU2020)

14 Review: Inductive Bias, Algorithmic Bias, Social impact of machine learning (MLVU2019)

14 Review: Inductive Bias, Algorithmic Bias, Social impact of machine learning (MLVU2019)

13 Reinforcement Learning: Policy Gradients, Q Learning, AlphaGo, AlphaStar (MLVU2019)

13 Reinforcement Learning: Policy Gradients, Q Learning, AlphaGo, AlphaStar (MLVU2019)

12 Matrix models: Recommender systems, PCA and Graph convolutions

12 Matrix models: Recommender systems, PCA and Graph convolutions

11 Sequential Data: Markov Models, Word Embeddings and LSTMs

11 Sequential Data: Markov Models, Word Embeddings and LSTMs

10 Tree Models and Ensembles: Decision Trees, AdaBoost, Gradient Boosting (MLVU2019)

10 Tree Models and Ensembles: Decision Trees, AdaBoost, Gradient Boosting (MLVU2019)

9 Deep Learning 2: Generative models, GANs, Variational Autoencoders (VAEs) (MLVU2019)

9 Deep Learning 2: Generative models, GANs, Variational Autoencoders (VAEs) (MLVU2019)

8 Probability 2: Maximum Likelihood, Gaussian Mixture Models and Expectation Maximization (MLVU2019)

8 Probability 2: Maximum Likelihood, Gaussian Mixture Models and Expectation Maximization (MLVU2019)

7 Deep learning 1: Backpropagation for tensors, Convolutional Neural Networks (MLVU2019)

7 Deep learning 1: Backpropagation for tensors, Convolutional Neural Networks (MLVU2019)

6 Linear Models 2: Neural Networks, Backpropagation, SVMs and Kernel methods (MLVU2019)

6 Linear Models 2: Neural Networks, Backpropagation, SVMs and Kernel methods (MLVU2019)

5 Probability 1: Entropy, (Naive) Bayes, Cross-entropy loss (MLVU2019)

5 Probability 1: Entropy, (Naive) Bayes, Cross-entropy loss (MLVU2019)

4 Methodology 2: Data cleaning, Principal Component Analysis, Eigenfaces (MLVU2019)

4 Methodology 2: Data cleaning, Principal Component Analysis, Eigenfaces (MLVU2019)

3 Methodology 1: Area-under-the-curve, bias and variance, no free lunch (MLVU2019)

3 Methodology 1: Area-under-the-curve, bias and variance, no free lunch (MLVU2019)

2 Linear Models 1: Hyperplanes, Random Search, Gradient Descent (MLVU2019)

2 Linear Models 1: Hyperplanes, Random Search, Gradient Descent (MLVU2019)

1 Introduction to Machine Learning (MLVU2019)

1 Introduction to Machine Learning (MLVU2019)

14 Review and outlook (MLVU2018)

14 Review and outlook (MLVU2018)

13 Reinforcement Learning: Policy Gradients, Q Learning, AlphaGo (MLVU2018)

13 Reinforcement Learning: Policy Gradients, Q Learning, AlphaGo (MLVU2018)

12 Matrix Models: Recommender systems and Matrix factorization (MLVU2018)

12 Matrix Models: Recommender systems and Matrix factorization (MLVU2018)

11 Models for Sequential Data: Markov Models, Word2Vec, RNNs and LSTMs.

11 Models for Sequential Data: Markov Models, Word2Vec, RNNs and LSTMs.

10 Tree Models and Ensembles: Decision Trees, Boosting, Bagging, Gradient Boosting (MLVU2018)

10 Tree Models and Ensembles: Decision Trees, Boosting, Bagging, Gradient Boosting (MLVU2018)

09 Deep Learning 2: GANs, Variational Autoencoders (MLVU2018)

09 Deep Learning 2: GANs, Variational Autoencoders (MLVU2018)

08 Probabilistic Models 2, Normal Distributions, Gaussian Mixtures and EM (MLVU2018)

08 Probabilistic Models 2, Normal Distributions, Gaussian Mixtures and EM (MLVU2018)

07 Linear Models 2: Support Vector Machines, the Kernel trick (MLVU2018)

07 Linear Models 2: Support Vector Machines, the Kernel trick (MLVU2018)

06 Deep Learning 1: Neural networks, Convolutional layers (MLVU2018)

06 Deep Learning 1: Neural networks, Convolutional layers (MLVU2018)

05 Probabilistic Models 1: Naive Bayes, Entropy, Logistic Regression (MLVU2018)

05 Probabilistic Models 1: Naive Bayes, Entropy, Logistic Regression (MLVU2018)

04 Methodology 2: Data cleaning, Principal Component Analysis (MLVU2018)

04 Methodology 2: Data cleaning, Principal Component Analysis (MLVU2018)

03 Methodology 1: ROC curves, Ranking classifiers (MLVU2018)

03 Methodology 1: ROC curves, Ranking classifiers (MLVU2018)

02 Linear Models 1: Random Search, Gradient Descent (MLVU2018)

02 Linear Models 1: Random Search, Gradient Descent (MLVU2018)

01 Introduction to Machine Learning (MLVU2018)

01 Introduction to Machine Learning (MLVU2018)