From f6017bc1ed51aae25ad6870db8cceda6827b3a0e Mon Sep 17 00:00:00 2001 From: franzi Date: Wed, 22 Sep 2021 10:31:53 +0200 Subject: [PATCH] update readme with new chapter links --- README.md | 36 ++++++++++++------------- exercises/3_supervised_comparison.ipynb | 8 +++--- 2 files changed, 22 insertions(+), 22 deletions(-) diff --git a/README.md b/README.md index e44dc42..462118f 100644 --- a/README.md +++ b/README.md @@ -24,7 +24,7 @@ For an optimal learning experience, the chapters from the [machine learning book ### Part 1: Getting started: What is ML? ##### Block 1.1: -- [ ] Read the whole chapter: ["Introduction: Solving Problems with ML"](https://franziskahorn.de/mlbook/_introduction_solving_problems_with_ml.html) +- [ ] Read the whole chapter: ["Introduction"](https://franziskahorn.de/mlbook/_introduction.html) - [ ] Answer [Quiz 1](https://forms.gle/uzdzytpsYf9sFG946) ##### Block 1.2: @@ -34,40 +34,41 @@ For an optimal learning experience, the chapters from the [machine learning book ##### Block 1.3: - [ ] Read the whole chapter: ["Data & Preprocessing"](https://franziskahorn.de/mlbook/_data_preprocessing.html) - [ ] Answer [Quiz 2](https://forms.gle/Pqr6EKHNxzrWb7MF9) -- [ ] Read the introductory part of the chapter ["ML Algorithms: Unsupervised & Supervised Learning"](https://franziskahorn.de/mlbook/_ml_algorithms_unsupervised_supervised_learning.html) + +##### Block 1.4: +- [ ] Read the whole chapter ["ML Solutions: Overview"](https://franziskahorn.de/mlbook/_ml_solutions_overview.html) +- [ ] Answer [Quiz 3](https://forms.gle/fr7PYmP9Exx4Vvrc8) --- ### Part 2: Your first algorithms ##### Block 2.1: -- [ ] Read the section: ["UL: Dimensionality Reduction"](https://franziskahorn.de/mlbook/_ul_dimensionality_reduction.html) -- [ ] Work through [Notebook 1: visualize text](/exercises/1_visualize_text.ipynb) +- [ ] Read the whole chapter: ["Unsupervised Learning"](https://franziskahorn.de/mlbook/_unsupervised_learning.html) +- [ ] Work through [Notebook 1: visualize text](/exercises/1_visualize_text.ipynb) (after the section on dimensionality reduction) +- [ ] Work through [Notebook 2: image quantization](/exercises/2_image_quantization.ipynb) (after the section on clustering) ##### Block 2.2: -- [ ] Read the section: ["UL: Outlier / Anomaly Detection"](https://franziskahorn.de/mlbook/_ul_outlier_anomaly_detection.html) -- [ ] Read the section: ["UL: Clustering"](https://franziskahorn.de/mlbook/_ul_clustering.html) -- [ ] Work through [Notebook 2: image quantization](/exercises/2_image_quantization.ipynb) - -##### Block 2.3: -- [ ] Read the section: ["Supervised Learning: Overview"](https://franziskahorn.de/mlbook/_supervised_learning_overview.html) -- [ ] Answer [Quiz 3](https://forms.gle/M2dDevwzicjcHLtc9) +- [ ] 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) +- [ ] Answer [Quiz 4](https://forms.gle/M2dDevwzicjcHLtc9) --- ### Part 3: Advanced models ##### Block 3.1: -- [ ] 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) +- [ ] 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) - [ ] **In parallel**, work through the respective sections of [Notebook 3: supervised comparison](/exercises/3_supervised_comparison.ipynb) ##### Block 3.2: -- [ ] 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) +- [ ] 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) - [ ] Work through [Notebook 4: information retrieval](/exercises/4_information_retrieval.ipynb) ##### Block 3.3: -- [ ] Read the section: ["SL: Neural Networks"](https://franziskahorn.de/mlbook/_sl_neural_networks.html) +- [ ] Read the section: ["Deep Learning (Neural Networks)"](https://franziskahorn.de/mlbook/_deep_learning_neural_networks.html) - [ ] 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) + +##### Block 3.4: - [ ] 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) --- @@ -78,7 +79,7 @@ For an optimal learning experience, the chapters from the [machine learning book - [ ] Read the whole chapter: ["Avoiding Common Pitfalls"](https://franziskahorn.de/mlbook/_avoiding_common_pitfalls.html) ##### Block 4.2: -- [ ] Answer [Quiz 4](https://forms.gle/uZGj54YQHKwckmL46) +- [ ] Answer [Quiz 5](https://forms.gle/uZGj54YQHKwckmL46) - [ ] Work through [Notebook 6: analyze toy dataset](/exercises/6_analyze_toydata.ipynb) ##### Block 4.3: @@ -89,12 +90,11 @@ For an optimal learning experience, the chapters from the [machine learning book ### Part 5: RL & Conclusion ##### Block 5.1: -- [ ] Read the whole chapter: ["ML Algorithms: Reinforcement Learning"](https://franziskahorn.de/mlbook/_ml_algorithms_reinforcement_learning.html) +- [ ] Read the whole chapter: ["Reinforcement Learning"](https://franziskahorn.de/mlbook/_reinforcement_learning.html) - [ ] Work through [Notebook 8: RL gridmove](/exercises/8_rl_gridmove.ipynb) ##### Block 5.2: -- [ ] Answer [Quiz 5](https://forms.gle/fr7PYmP9Exx4Vvrc8) -- [ ] Read the whole chapter: ["Conclusion: Using ML in Practice"](https://franziskahorn.de/mlbook/_conclusion_using_ml_in_practice.html) +- [ ] Read the whole chapter: ["Conclusion"](https://franziskahorn.de/mlbook/_conclusion.html) - [ ] Complete the exercise: ["Your next ML Project"](/exercise_your_ml_project.pdf) --- diff --git a/exercises/3_supervised_comparison.ipynb b/exercises/3_supervised_comparison.ipynb index 3feb009..15fe063 100644 --- a/exercises/3_supervised_comparison.ipynb +++ b/exercises/3_supervised_comparison.ipynb @@ -187,10 +187,10 @@ "source": [ "## Linear Models\n", "\n", - "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", + "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", "\n", "**Questions:**\n", - "- Compare the linear regression and ridge regression models on the regression dataset with outliers: what do you observe?\n", + "- 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", "- 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)" ] }, @@ -210,7 +210,7 @@ "outputs": [], "source": [ "# Linear Regression\n", - "X, y = X_reg_2, y_reg_2 # change the numbers here to test the model on a different dataset\n", + "X, y = X_reg_1, y_reg_1 # change the numbers here to test the model on a different dataset\n", "model = LinearRegression()\n", "model.fit(X, y)\n", "plot_regression(X, y, model)\n", @@ -225,7 +225,7 @@ "source": [ "# Ridge Regression:\n", "# alpha (> 0): regularization (higher values = more regularization)\n", - "X, y = X_reg_2, y_reg_2\n", + "X, y = X_reg_1, y_reg_1\n", "model = Ridge(alpha=1.)\n", "model.fit(X, y)\n", "plot_regression(X, y, model)\n",