Lectures
- Syllabus
- slides
- Introduction
- slides, reference: 1 [Murphy22]
- Parameter Estimation
-
slides,
notebook
(Colab), reference: 4 [Murphy22], 2-2.3 [Bishop], 3-3.5 [Murphy]
- Linear regression
-
slides,
notebook
(Colab),
reference: 7-7.3.3 [Murphy], 3-3.1.2 [Bishop]
- Logistic and softmax regression
-
slides,
notebook
(Colab),
reference: 8.1-8.3.3 [Murphy], 4.1-4.1.3 + 4.3-4.3.3 [Bishop]
- Gradient descent methods
-
slides,
notebook
(Colab),
reference: 8.3.2 [Murphy] and
this overview by S. Ruder (in pdf )
- Regularization
-
slides,
notebook
(Colab),
reference: 3.1.4-3.3 [Bishop]
- Generalization
-
slides,
notebook for model selection (Colab), second notebook for curse of dimensionality
(Colab)
- Review 1
-
slides,
- Perceptrons & multilayer perceptrons
-
slides,
Perceptrons Colab, MLP demo,
reference: 4.1.1-4.1.3 + 4.1.7 [Bishop], 6-6.5 + parts of 7 [Goodfellow]
- Gradient computation and automatic differentiation
-
slides,
notebook
(Colab),
reference: 6.5 + 8.2 [Goodfellow], blog post, visualization
- Convolutional neural networks
-
slides,
notebook
(Colab),
reference: 9 [Goodfellow], blog post, optional reading , interactive demo
- Neural Networks for Sequences: Recurrent Neural Networks
-
slides,
notebook
(Colab), reference:
15.2 [Murphy'22]
For a quick understand of the LSTM, the prototypical Recurrent NeuralNet for sequences, see this great blogpost.
- Neural Networks for Sequences: Attention and Transformers
-
slides,
notebook
(Colab),
reference: 15.4 [Murphy'22], optional reading ,
- Naive Bayes
-
slides,
notebook
(Colab),
reference: 3.5-3.5.4 [Murphy]
- Nearest neighbours
-
slides,
notebook (Colab), reference: chapter 1 [Murphy]
- Classification and regression trees
-
slides,
notebook
(Colab), reference: 16.1-16.2.6 [Murphy], 14.4 [Bishop]
- Bagging & boosting
-
slides,
notebook
(Colab),
reference: 3.2 [Bishop], 18.3, 18.4, 18.5 [Murphy'22] demos for
Bias-Variance Tradeoff,
Gradient Boosting explanation, and
Interactive playground
- Unsupervised learning
-
slides,
notebook
(Colab),
reference: 25.5 [Murphy] and 9.1 [Bishop],
demos for K-Means and DB-SCAN
- Dimensionality reduction
-
slides,
notebook
(Colab),
reference: 12.2 [Murphy], 12.1 [Bishop], demo