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update readme with new chapter links
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franzi
36
README.md
36
README.md
@@ -24,7 +24,7 @@ For an optimal learning experience, the chapters from the [machine learning book
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### Part 1: Getting started: What is ML?
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##### Block 1.1:
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- [ ] Read the whole chapter: ["Introduction: Solving Problems with ML"](https://franziskahorn.de/mlbook/_introduction_solving_problems_with_ml.html)
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- [ ] Read the whole chapter: ["Introduction"](https://franziskahorn.de/mlbook/_introduction.html)
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- [ ] Answer [Quiz 1](https://forms.gle/uzdzytpsYf9sFG946)
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##### Block 1.2:
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@@ -34,40 +34,41 @@ For an optimal learning experience, the chapters from the [machine learning book
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##### Block 1.3:
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- [ ] Read the whole chapter: ["Data & Preprocessing"](https://franziskahorn.de/mlbook/_data_preprocessing.html)
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- [ ] Answer [Quiz 2](https://forms.gle/Pqr6EKHNxzrWb7MF9)
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- [ ] Read the introductory part of the chapter ["ML Algorithms: Unsupervised & Supervised Learning"](https://franziskahorn.de/mlbook/_ml_algorithms_unsupervised_supervised_learning.html)
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##### Block 1.4:
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- [ ] Read the whole chapter ["ML Solutions: Overview"](https://franziskahorn.de/mlbook/_ml_solutions_overview.html)
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- [ ] Answer [Quiz 3](https://forms.gle/fr7PYmP9Exx4Vvrc8)
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---
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### Part 2: Your first algorithms
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##### Block 2.1:
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- [ ] Read the section: ["UL: Dimensionality Reduction"](https://franziskahorn.de/mlbook/_ul_dimensionality_reduction.html)
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- [ ] Work through [Notebook 1: visualize text](/exercises/1_visualize_text.ipynb)
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- [ ] Read the whole chapter: ["Unsupervised Learning"](https://franziskahorn.de/mlbook/_unsupervised_learning.html)
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- [ ] Work through [Notebook 1: visualize text](/exercises/1_visualize_text.ipynb) (after the section on dimensionality reduction)
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- [ ] Work through [Notebook 2: image quantization](/exercises/2_image_quantization.ipynb) (after the section on clustering)
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##### Block 2.2:
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- [ ] Read the section: ["UL: Outlier / Anomaly Detection"](https://franziskahorn.de/mlbook/_ul_outlier_anomaly_detection.html)
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- [ ] Read the section: ["UL: Clustering"](https://franziskahorn.de/mlbook/_ul_clustering.html)
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- [ ] Work through [Notebook 2: image quantization](/exercises/2_image_quantization.ipynb)
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##### Block 2.3:
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- [ ] Read the section: ["Supervised Learning: Overview"](https://franziskahorn.de/mlbook/_supervised_learning_overview.html)
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- [ ] Answer [Quiz 3](https://forms.gle/M2dDevwzicjcHLtc9)
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- [ ] Read the first sections of the chapter ["Supervised Learning"](https://franziskahorn.de/mlbook/_supervised_learning.html) up to and including ["Model Evaluation"](https://franziskahorn.de/mlbook/_model_evaluation.html)
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- [ ] Answer [Quiz 4](https://forms.gle/M2dDevwzicjcHLtc9)
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---
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### Part 3: Advanced models
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##### Block 3.1:
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- [ ] Read the sections: ["SL: Linear Models"](https://franziskahorn.de/mlbook/_sl_linear_models.html) up to and including ["SL: Kernel Methods"](https://franziskahorn.de/mlbook/_sl_kernel_methods.html)
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- [ ] Read the remaining sections from the supervised learning chapter, i.e., ["Linear Models"](https://franziskahorn.de/mlbook/_linear_models.html) up to and including ["Kernel Methods"](https://franziskahorn.de/mlbook/_kernel_methods.html)
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- [ ] **In parallel**, work through the respective sections of [Notebook 3: supervised comparison](/exercises/3_supervised_comparison.ipynb)
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##### Block 3.2:
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- [ ] Read the section: ["Information Retrieval (Similarity Search)"](https://franziskahorn.de/mlbook/_information_retrieval_similarity_search.html) and review the sections on [TF-IDF feature vectors](https://franziskahorn.de/mlbook/_feature_extraction.html) and [cosine similarity](https://franziskahorn.de/mlbook/_computing_similarities.html)
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- [ ] Start with the chapter ["Deep Learning & more"](https://franziskahorn.de/mlbook/_deep_learning_more.html) up to and including the section: ["Information Retrieval (Similarity Search)"](https://franziskahorn.de/mlbook/_information_retrieval_similarity_search.html) and refresh your memory on the sections on [TF-IDF feature vectors](https://franziskahorn.de/mlbook/_feature_extraction.html) and [cosine similarity](https://franziskahorn.de/mlbook/_computing_similarities.html)
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- [ ] Work through [Notebook 4: information retrieval](/exercises/4_information_retrieval.ipynb)
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##### Block 3.3:
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- [ ] Read the section: ["SL: Neural Networks"](https://franziskahorn.de/mlbook/_sl_neural_networks.html)
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- [ ] Read the section: ["Deep Learning (Neural Networks)"](https://franziskahorn.de/mlbook/_deep_learning_neural_networks.html)
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- [ ] Work through [Notebook 5: MNIST with torch](/exercises/5_mnist_torch.ipynb) (recommended) **_or_** [MNIST with keras](/exercises/5_mnist_keras.ipynb) (in case others in your organization are already working with TensorFlow)
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##### Block 3.4:
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- [ ] Read the sections: ["Time Series Forecasting"](https://franziskahorn.de/mlbook/_time_series_forecasting.html) and ["Recommender Systems (Pairwise Data)"](https://franziskahorn.de/mlbook/_recommender_systems_pairwise_data.html)
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---
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@@ -78,7 +79,7 @@ For an optimal learning experience, the chapters from the [machine learning book
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- [ ] Read the whole chapter: ["Avoiding Common Pitfalls"](https://franziskahorn.de/mlbook/_avoiding_common_pitfalls.html)
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##### Block 4.2:
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- [ ] Answer [Quiz 4](https://forms.gle/uZGj54YQHKwckmL46)
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- [ ] Answer [Quiz 5](https://forms.gle/uZGj54YQHKwckmL46)
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- [ ] Work through [Notebook 6: analyze toy dataset](/exercises/6_analyze_toydata.ipynb)
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##### Block 4.3:
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@@ -89,12 +90,11 @@ For an optimal learning experience, the chapters from the [machine learning book
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### Part 5: RL & Conclusion
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##### Block 5.1:
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- [ ] Read the whole chapter: ["ML Algorithms: Reinforcement Learning"](https://franziskahorn.de/mlbook/_ml_algorithms_reinforcement_learning.html)
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- [ ] Read the whole chapter: ["Reinforcement Learning"](https://franziskahorn.de/mlbook/_reinforcement_learning.html)
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- [ ] Work through [Notebook 8: RL gridmove](/exercises/8_rl_gridmove.ipynb)
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##### Block 5.2:
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- [ ] Answer [Quiz 5](https://forms.gle/fr7PYmP9Exx4Vvrc8)
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- [ ] Read the whole chapter: ["Conclusion: Using ML in Practice"](https://franziskahorn.de/mlbook/_conclusion_using_ml_in_practice.html)
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- [ ] Read the whole chapter: ["Conclusion"](https://franziskahorn.de/mlbook/_conclusion.html)
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- [ ] Complete the exercise: ["Your next ML Project"](/exercise_your_ml_project.pdf)
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---
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@@ -187,10 +187,10 @@
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"source": [
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"## Linear Models\n",
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"\n",
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"After reading the chapter on linear models, test them here on different datasets (by changing the number at the end of the dataset variable, e.g., `X_reg_2` -> `X_reg_3`) and experiment with their hyperparameter settings (in the comments you'll find a description of the different hyperparameters and which values you can test for them).\n",
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"After reading the chapter on linear models, test them here on different datasets (by changing the number at the end of the dataset variable, e.g., `X_reg_1` -> `X_reg_2`) and experiment with their hyperparameter settings (in the comments you'll find a description of the different hyperparameters and which values you can test for them).\n",
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"\n",
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"**Questions:**\n",
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"- Compare the linear regression and ridge regression models on the regression dataset with outliers: what do you observe?\n",
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"- Compare the linear regression and ridge regression models on the regression dataset with outliers (i.e., `X_reg_2, y_reg_2`): what do you observe?\n",
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"- What happens when you increase the value for `alpha` for the ridge regression model? (first think about it, then confirm your guess by actually changing the parameter)"
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]
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},
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@@ -210,7 +210,7 @@
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"outputs": [],
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"source": [
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"# Linear Regression\n",
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"X, y = X_reg_2, y_reg_2 # change the numbers here to test the model on a different dataset\n",
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"X, y = X_reg_1, y_reg_1 # change the numbers here to test the model on a different dataset\n",
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"model = LinearRegression()\n",
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"model.fit(X, y)\n",
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"plot_regression(X, y, model)\n",
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@@ -225,7 +225,7 @@
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"source": [
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"# Ridge Regression:\n",
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"# alpha (> 0): regularization (higher values = more regularization)\n",
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"X, y = X_reg_2, y_reg_2\n",
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"X, y = X_reg_1, y_reg_1\n",
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"model = Ridge(alpha=1.)\n",
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"model.fit(X, y)\n",
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"plot_regression(X, y, model)\n",
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