Ovcharenko Group Wiki :: Main / DeepLearning

Deep Learning

Introduction to machine learning pt. 1 (46 min) Introduction pt. 1

Introduction to machine learning pt. 2 (50 min) Introduction pt. 2

Basic Probability (53 min) Probability

Linear Algebra (53 min) Linear Algebra

Baye's Theorem pt. 1 (9 min) Baye's Theorem pt. 1

Baye's Theorem pt. 2 (51 min) Baye's Theorem pt. 2

Bayesian Inference (49 min) Bayesian Inference

Hidden Markov Models (49 min) HMM

Expectation & Bernoulli models (42 min) Bernoulli models

Maximum Likelihood (42 min) ML

Bayesian Learning pt. 1 (53 min) Bayesian Learning pt. 1

Bayesian Learning pt. 2 (53 min) Bayesian Learning pt. 2

Linear algebra for machine learning (53 min) Linear Algebra

Singular Value Decomposition (51 min) SVD

Principle Component Analysis (51 min) PCA

Least squares & multivariate Gaussian (42 min) Least squares

Graphical Models (45 min) Graphical Models

Linear Prediction (51 min) Linear Prediction

Cross Validation (90 min) CV & Regularization

L1 Regularization and LASSO (20 min) LASSO pt. 1

Variable Selection and LASSO (71 min) LASSO pt. 2

Dirichlet Distribution (46 min) Dirichlet

Text Classification (43 min) Naive Bayes pt. 1

Naive Bayes - Applications (46 min) Naive Bayes pt. 2

Gradient Descent (49 min) Optimization

Logistic Regression (51 min) Regression

Neural networks pt. 1 (26 min) NNs pt. 1

Neural networks pt. 2 (40 min) NNs pt. 2

Deep Learning (50 min) Deep Learning

Decision Trees (39 min) Decision Trees

Random Forests pt. 1 (33 min) Random Forests pt. 1

Random Forests pt. 2 (38 min) Random Forests pt. 2