Machine Learning for Engineers
Lecture 8.1 - Tokenization and embeddings
40:09
Machine Learning for Engineers
Lecture 8.2 - Seq2seq models and self-attention
34:34
Machine Learning for Engineers
Lecture 8.4 - Vision Transformers (from scratch)
12:56
Machine Learning for Engineers
Lecture 8.3 - Transformer models
16:42
Machine Learning for Engineers
Lecture 7.7 - Transfer Learning
15:38
Machine Learning for Engineers
Lecture 7.6 - Visualizing what models look at
10:05
Machine Learning for Engineers
Lecture 7.5 - Visualizing what models learn
35:45
Machine Learning for Engineers
Lecture 7.4 - Famous Convolutional Networks
17:39
Machine Learning for Engineers
Lecture 7.3 - Building Convolutional Networks
31:46
Machine Learning for Engineers
Lecture 7.2 - Convolutional Neural Networks
20:23
Machine Learning for Engineers
Lecture 7.1 - Convolutions
10:46
Machine Learning for Engineers
Welcome
8:45
Machine Learning for Engineers
Extra Lecture - Meta-Learning
1:32:50
Machine Learning for Engineers
Extra Lecture - Gaussian Processes in practice
10:43
Machine Learning for Engineers
Extra Lecture - Gaussian Processes
1:02:16
Machine Learning for Engineers
Extra Lecture - Naive Bayes and Bayesian Networks
17:39
Machine Learning for Engineers
Extra Lecture - Bayesian Learning
17:07
Machine Learning for Engineers
Lecture 8.2 - Word embeddings (old version)
46:31
Machine Learning for Engineers
Lecture 8.1 - Neural Networks for text (old version)
28:44
Machine Learning for Engineers
Lecture 6.6 - Model selection and regularization
26:15
Machine Learning for Engineers
Lecture 6.4 - Neural network optimizers
35:38
Machine Learning for Engineers
Lecture 6.5 - Neural networks in practice
22:52
Machine Learning for Engineers
Lecture 6.3 - Activation functions and weight initialization
24:07
Machine Learning for Engineers
Lecture 6.2 - Training neural nets
33:31
Machine Learning for Engineers
Lecture 6.1 - Introduction to neural networks
22:18
Machine Learning for Engineers
Lecture 5.8 - Handling imbalanced data
28:46
Machine Learning for Engineers
Lecture 5.7 - Missing value imputation
25:32
Machine Learning for Engineers
Lecture 5.4 - Automatic Feature Selection
45:44
Machine Learning for Engineers
Lecture 5.5 - Automatic Feature Selection in practice
14:25
Machine Learning for Engineers
Lecture 5.6 - Feature Engineering
9:46
Machine Learning for Engineers
Lecture 5.1 - Introduction to data preprocessing
26:59
Machine Learning for Engineers
Lecture 5.3 - Preprocessing in practice
19:22
Machine Learning for Engineers
Lecture 5.2 - Categorical feature encoding
11:35
Machine Learning for Engineers
Lecture 4.2: Ensemble Learning - Part 2
1:08:35
Machine Learning for Engineers
Lecture 4.1: Ensemble Learning
52:10
Machine Learning for Engineers
Lecture 3.2: Model Selection - Part 2
58:37
Machine Learning for Engineers
Lecture 3.1: Model Selection
1:18:05
Machine Learning for Engineers
Extra Lecture: Kernelization
1:13:05
Machine Learning for Engineers
Lecture 2.2: Linear models for classification
1:15:04
Machine Learning for Engineers
Lecture 2.1: Linear models for regression
1:10:38
Machine Learning for Engineers
Lecture 2.0 - Mathematical notation and definitions
17:05
Machine Learning for Engineers
Welcome
14:27
Machine Learning for Engineers
Lecture 1: Introduction to Machine Learning
1:23:57
Machine Learning for Engineers
Guest Lecture: AI-designed Whisky and Algoritmic Trading
1:11:16
Machine Learning for Engineers
Extra Lecture: Automated Machine Learning and Meta-Learning
2:17:54
Machine Learning for Engineers
Lecture 7: Convolutional Neural Networks (old version)
1:11:50