Exercises week 37#
+Exercises week 38#
September 15-19#
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- Week 36: Linear Regression and Gradient descent
- Exercises week 37
- Week 37: Gradient descent methods
-- Exercises week 37
+- Exercises week 38
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@@ -230,7 +230,7 @@
- Week 36: Linear Regression and Gradient descent
- Exercises week 37
- Week 37: Gradient descent methods -
- Exercises week 37 +
- Exercises week 38
Projects
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diff --git a/doc/LectureNotes/_build/html/searchindex.js b/doc/LectureNotes/_build/html/searchindex.js
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"mathematics-of-the-svd-and-implications"], [28, "mathematics-of-the-svd-and-implications"], [29, "mathematics-of-the-svd-and-implications"]], "Matrices in Python": [[27, "matrices-in-python"]], "Matrix multiplication": [[1, "matrix-multiplication"]], "Matrix-vector notation and activation": [[12, "matrix-vector-notation-and-activation"]], "Meet the covariance!": [[24, "meet-the-covariance"]], "Meet the Covariance Matrix": [[5, "meet-the-covariance-matrix"], [28, "meet-the-covariance-matrix"]], "Meet the Hessian Matrix": [[28, "meet-the-hessian-matrix"]], "Meet the Pandas": [[27, "meet-the-pandas"]], "Min-Max Scaling": [[28, "min-max-scaling"]], "Momentum based GD": [[13, "momentum-based-gd"], [30, "momentum-based-gd"]], "More complicated Example: The Ising model": [[6, "more-complicated-example-the-ising-model"]], "More interpretations": [[28, "more-interpretations"], [29, "more-interpretations"], [29, "id5"]], "More on Dimensionalities": [[3, "more-on-dimensionalities"]], "More on 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LASSO Regression": [[28, "back-to-ridge-and-lasso-regression"], [29, "back-to-ridge-and-lasso-regression"]], "Back to the Cancer Data": [[11, "back-to-the-cancer-data"]], "Background literature": [[22, "background-literature"]], "Bagging": [[10, "bagging"]], "Bagging Examples": [[10, "bagging-examples"]], "Basic Matrix Features": [[21, "basic-matrix-features"]], "Basic ideas of the Principal Component Analysis (PCA)": [[11, null]], "Basic math of the SVD": [[5, "basic-math-of-the-svd"], [28, "basic-math-of-the-svd"], [29, "basic-math-of-the-svd"]], "Basics": [[7, "basics"]], "Basics of a tree": [[9, "basics-of-a-tree"]], "Batch Normalization": [[1, "batch-normalization"]], "Batches and mini-batches": [[30, "batches-and-mini-batches"]], "Bayes\u2019 Theorem and Ridge and Lasso Regression": [[5, "bayes-theorem-and-ridge-and-lasso-regression"]], "Boosting, a Bird\u2019s Eye View": [[10, "boosting-a-bird-s-eye-view"]], "Bootstrap": [[6, "bootstrap"]], "Bringing it together, first back 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"Clustering and Unsupervised Learning": [[14, null]], "Code for SVD and Inversion of Matrices": [[5, "code-for-svd-and-inversion-of-matrices"]], "Code with a Number of Minibatches which varies": [[30, "code-with-a-number-of-minibatches-which-varies"]], "Codes and Approaches": [[14, "codes-and-approaches"]], "Codes for the SVD": [[5, "codes-for-the-svd"], [28, "codes-for-the-svd"], [29, "codes-for-the-svd"]], "Coding Setup and Linear Regression": [[15, "coding-setup-and-linear-regression"]], "Collect and pre-process data": [[1, "collect-and-pre-process-data"]], "Communication channels": [[27, "communication-channels"]], "Compare Bagging on Trees with Random Forests": [[10, "compare-bagging-on-trees-with-random-forests"]], "Comparing with a numerical scheme": [[2, "comparing-with-a-numerical-scheme"]], "Comparison with OLS": [[29, "comparison-with-ols"]], "Computation of gradients": [[30, "computation-of-gradients"]], "Computing the Gini index": [[9, "computing-the-gini-index"]], "Conditions on convex functions": [[29, "conditions-on-convex-functions"]], "Conjugate gradient method": [[13, "conjugate-gradient-method"]], "Convergence rates": [[30, "convergence-rates"]], "Convex function": [[29, "convex-function"]], "Convex functions": [[13, "convex-functions"], [29, "convex-functions"]], "Convolution Examples: Polynomial multiplication": [[3, "convolution-examples-polynomial-multiplication"]], "Convolution Examples: Principle of Superposition and Periodic Forces (Fourier Transforms)": [[3, "convolution-examples-principle-of-superposition-and-periodic-forces-fourier-transforms"]], "Convolutional Neural Network": [[12, "convolutional-neural-network"]], "Convolutional Neural Networks": [[3, null]], "Correlation Function and Design/Feature Matrix": [[28, "correlation-function-and-design-feature-matrix"]], "Correlation Matrix": [[11, "correlation-matrix"], [28, "correlation-matrix"]], "Correlation Matrix with Pandas": [[28, "correlation-matrix-with-pandas"]], "Course 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Implementing Ordinary Least Squares": [[16, "deriving-and-implementing-ordinary-least-squares"]], "Deriving and Implementing Ridge Regression": [[17, "deriving-and-implementing-ridge-regression"]], "Deriving the Lasso Regression Equations": [[28, "deriving-the-lasso-regression-equations"], [29, "deriving-the-lasso-regression-equations"], [29, "id6"]], "Deriving the Ridge Regression Equations": [[28, "deriving-the-ridge-regression-equations"], [29, "deriving-the-ridge-regression-equations"], [29, "id3"]], "Deriving the back propagation code for a multilayer perceptron model": [[12, "deriving-the-back-propagation-code-for-a-multilayer-perceptron-model"]], "Developing a code for doing neural networks with back propagation": [[1, "developing-a-code-for-doing-neural-networks-with-back-propagation"]], "Diagonalize the sample covariance matrix to obtain the principal components": [[11, "diagonalize-the-sample-covariance-matrix-to-obtain-the-principal-components"]], "Different kernels and Mercer\u2019s theorem": [[8, "different-kernels-and-mercer-s-theorem"]], "Disadvantages": [[9, "disadvantages"]], "Discriminative Modeling": [[27, "discriminative-modeling"]], "Domains and probabilities": [[24, "domains-and-probabilities"]], "Dropout": [[1, "dropout"]], "Economy-size SVD": [[28, "economy-size-svd"], [29, "economy-size-svd"]], "Elements of Probability Theory and Statistical Data Analysis": [[24, null]], "Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods": [[10, null]], "Entropy and the ID3 algorithm": [[9, "entropy-and-the-id3-algorithm"]], "Essential elements of ML": [[27, "essential-elements-of-ml"]], "Evaluate model performance on test data": [[1, "evaluate-model-performance-on-test-data"]], "Example 2": [[28, "example-2"]], "Example 3": [[28, "example-3"]], "Example 4": [[28, "example-4"]], "Example Matrix": [[28, "example-matrix"], [29, "example-matrix"]], "Example of discriminative modeling, taken from Generative Deep Learning by David Foster": [[27, "example-of-discriminative-modeling-taken-from-generative-deep-learning-by-david-foster"]], "Example of generative modeling, taken from Generative Deep Learning by David Foster": [[27, "example-of-generative-modeling-taken-from-generative-deep-learning-by-david-foster"]], "Example of own Standard scaling": [[28, "example-of-own-standard-scaling"]], "Example relevant for the exercises": [[28, "example-relevant-for-the-exercises"]], "Example: Exponential decay": [[2, "example-exponential-decay"]], "Example: Population growth": [[2, "example-population-growth"]], "Example: The diffusion equation": [[2, "example-the-diffusion-equation"]], "Example: binary classification problem": [[1, "example-binary-classification-problem"]], "Examples": [[27, "examples"]], "Examples of likelihood functions used in logistic regression and neural networks": [[7, "examples-of-likelihood-functions-used-in-logistic-regression-and-neural-networks"]], "Exercise 1 - Choice of model and degrees of freedom": [[17, "exercise-1-choice-of-model-and-degrees-of-freedom"]], "Exercise 1 - Finding the derivative of Matrix-Vector expressions": [[16, "exercise-1-finding-the-derivative-of-matrix-vector-expressions"]], "Exercise 1 - Github Setup": [[15, "exercise-1-github-setup"]], "Exercise 1, scale your data": [[18, "exercise-1-scale-your-data"]], "Exercise 1: Expectation values for ordinary least squares expressions": [[19, "exercise-1-expectation-values-for-ordinary-least-squares-expressions"]], "Exercise 1: Setting up various Python environments": [[0, "exercise-1-setting-up-various-python-environments"]], "Exercise 2 - Deriving the expression for OLS": [[16, "exercise-2-deriving-the-expression-for-ols"]], "Exercise 2 - Deriving the expression for Ridge Regression": [[17, "exercise-2-deriving-the-expression-for-ridge-regression"]], "Exercise 2 - Setting up a Github repository": [[15, "exercise-2-setting-up-a-github-repository"]], "Exercise 2, calculate the gradients": [[18, "exercise-2-calculate-the-gradients"]], "Exercise 2: Expectation values for Ridge regression": [[19, "exercise-2-expectation-values-for-ridge-regression"]], "Exercise 2: making your own data and exploring scikit-learn": [[0, "exercise-2-making-your-own-data-and-exploring-scikit-learn"]], "Exercise 3 - Creating feature matrix and implementing OLS using the analytical expression": [[16, "exercise-3-creating-feature-matrix-and-implementing-ols-using-the-analytical-expression"]], "Exercise 3 - Fitting an OLS model to data": [[15, "exercise-3-fitting-an-ols-model-to-data"]], "Exercise 3 - Scaling data": [[17, "exercise-3-scaling-data"]], "Exercise 3 - Setting up a Python virtual environment": [[15, "exercise-3-setting-up-a-python-virtual-environment"]], "Exercise 3, using the analytical formulae for OLS and Ridge regression to find the optimal paramters \\boldsymbol{\\theta}": [[18, "exercise-3-using-the-analytical-formulae-for-ols-and-ridge-regression-to-find-the-optimal-paramters-boldsymbol-theta"]], "Exercise 3: Deriving the expression for the Bias-Variance Trade-off": [[19, "exercise-3-deriving-the-expression-for-the-bias-variance-trade-off"]], "Exercise 3: Normalizing our data": [[0, "exercise-3-normalizing-our-data"]], "Exercise 4 - Fitting a polynomial": [[16, "exercise-4-fitting-a-polynomial"]], "Exercise 4 - Implementing Ridge Regression": [[17, "exercise-4-implementing-ridge-regression"]], "Exercise 4 - Testing multiple hyperparameters": [[17, "exercise-4-testing-multiple-hyperparameters"]], "Exercise 4 - The train-test split": [[15, "exercise-4-the-train-test-split"]], "Exercise 4, Implementing the simplest form for gradient descent": [[18, "exercise-4-implementing-the-simplest-form-for-gradient-descent"]], "Exercise 4: Adding Ridge Regression": [[0, "exercise-4-adding-ridge-regression"]], "Exercise 4: Computing the Bias and Variance": [[19, "exercise-4-computing-the-bias-and-variance"]], "Exercise 5 - Comparing your code with sklearn": [[16, "exercise-5-comparing-your-code-with-sklearn"]], "Exercise 5, Ridge regression and a new Synthetic Dataset": [[18, "exercise-5-ridge-regression-and-a-new-synthetic-dataset"]], "Exercise 5: Analytical exercises": [[0, "exercise-5-analytical-exercises"]], "Exercise: Cross-validation as resampling techniques, adding more complexity": [[6, "exercise-cross-validation-as-resampling-techniques-adding-more-complexity"]], "Exercise: Analysis of real data": [[6, "exercise-analysis-of-real-data"]], "Exercise: Bias-variance trade-off and resampling techniques": [[6, "exercise-bias-variance-trade-off-and-resampling-techniques"]], "Exercise: Lasso Regression on the Franke function with resampling": [[6, "exercise-lasso-regression-on-the-franke-function-with-resampling"]], "Exercise: Ordinary Least Square (OLS) on the Franke function": [[6, "exercise-ordinary-least-square-ols-on-the-franke-function"]], "Exercise: Ridge Regression on the Franke function with resampling": [[6, "exercise-ridge-regression-on-the-franke-function-with-resampling"]], "Exercises": [[0, "exercises"]], "Exercises and Projects": [[6, "exercises-and-projects"]], "Exercises week 34": [[15, null]], "Exercises week 35": [[16, null]], "Exercises week 36": [[17, null]], "Exercises week 37": [[18, null]], "Exercises week 38": [[19, null]], "Expectation values": [[24, "expectation-values"]], "Extending to more than one variable": [[29, "extending-to-more-than-one-variable"]], "Extremely useful tools, strongly recommended": [[27, "extremely-useful-tools-strongly-recommended"]], "Feed-forward neural networks": [[12, "feed-forward-neural-networks"]], "Feed-forward pass": [[1, "feed-forward-pass"]], "Final back propagating equation": [[12, "final-back-propagating-equation"]], "Fine-tuning neural network hyperparameters": [[1, "fine-tuning-neural-network-hyperparameters"]], "Fitting an Equation of State for Dense Nuclear Matter": [[0, "fitting-an-equation-of-state-for-dense-nuclear-matter"]], "Fixing the singularity": [[28, "fixing-the-singularity"], [29, "fixing-the-singularity"]], "Format for electronic delivery of report and programs": [[22, "format-for-electronic-delivery-of-report-and-programs"]], "Frequently used scaling functions": [[28, "frequently-used-scaling-functions"], [30, "frequently-used-scaling-functions"]], "From OLS to Ridge and Lasso": [[29, "from-ols-to-ridge-and-lasso"]], "From one to many layers, the universal approximation theorem": [[12, "from-one-to-many-layers-the-universal-approximation-theorem"]], "Functionality in Scikit-Learn": [[28, "functionality-in-scikit-learn"], [30, "functionality-in-scikit-learn"]], "Further Dimensionality Remarks": [[3, "further-dimensionality-remarks"]], "Further properties (important for our analyses later)": [[5, 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"gradient-boosting-classification-example"]], "Gradient Boosting, Examples of Regression": [[10, "gradient-boosting-examples-of-regression"]], "Gradient Clipping": [[1, "gradient-clipping"]], "Gradient Descent Example": [[29, "id1"], [30, "id1"]], "Gradient boosting: Basics with Steepest Descent/Functional Gradient Descent": [[10, "gradient-boosting-basics-with-steepest-descent-functional-gradient-descent"]], "Gradient descent": [[2, "gradient-descent"]], "Gradient descent and Ridge": [[29, "gradient-descent-and-ridge"], [30, "gradient-descent-and-ridge"]], "Gradient descent and revisiting Ordinary Least Squares from last week": [[30, "gradient-descent-and-revisiting-ordinary-least-squares-from-last-week"]], "Gradient descent example": [[29, "gradient-descent-example"], [30, "gradient-descent-example"]], "Grading": [[25, "grading"], [25, "id2"], [27, "grading"]], "How to take derivatives of Matrix-Vector expressions": [[16, "how-to-take-derivatives-of-matrix-vector-expressions"]], 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"making-your-own-bootstrap-changing-the-level-of-the-decision-tree"]], "Making your own test-train splitting": [[28, "making-your-own-test-train-splitting"]], "Material for exercises week 35": [[28, "material-for-exercises-week-35"]], "Material for lab sessions sessions Tuesday and Wednesday": [[29, "material-for-lab-sessions-sessions-tuesday-and-wednesday"]], "Material for lecture Monday September 2": [[29, "material-for-lecture-monday-september-2"]], "Material for lecture Monday September 8": [[30, "material-for-lecture-monday-september-8"]], "Material for the lab sessions": [[30, "material-for-the-lab-sessions"]], "Mathematical Interpretation of Ordinary Least Squares": [[5, "mathematical-interpretation-of-ordinary-least-squares"], [28, "mathematical-interpretation-of-ordinary-least-squares"], [29, "mathematical-interpretation-of-ordinary-least-squares"]], "Mathematical optimization of convex functions": [[8, "mathematical-optimization-of-convex-functions"]], "Mathematics of CNNs": [[3, "mathematics-of-cnns"]], "Mathematics of the SVD and implications": [[5, "mathematics-of-the-svd-and-implications"], [28, "mathematics-of-the-svd-and-implications"], [29, "mathematics-of-the-svd-and-implications"]], "Matrices in Python": [[27, "matrices-in-python"]], "Matrix multiplication": [[1, "matrix-multiplication"]], "Matrix-vector notation and activation": [[12, "matrix-vector-notation-and-activation"]], "Meet the covariance!": [[24, "meet-the-covariance"]], "Meet the Covariance Matrix": [[5, "meet-the-covariance-matrix"], [28, "meet-the-covariance-matrix"]], "Meet the Hessian Matrix": [[28, "meet-the-hessian-matrix"]], "Meet the Pandas": [[27, "meet-the-pandas"]], "Min-Max Scaling": [[28, "min-max-scaling"]], "Momentum based GD": [[13, "momentum-based-gd"], [30, "momentum-based-gd"]], "More complicated Example: The Ising model": [[6, "more-complicated-example-the-ising-model"]], "More interpretations": [[28, "more-interpretations"], [29, "more-interpretations"], [29, "id5"]], "More on Dimensionalities": [[3, "more-on-dimensionalities"]], "More on Rescaling data": [[6, "more-on-rescaling-data"]], "More on Steepest descent": [[29, "more-on-steepest-descent"]], "More on convex functions": [[29, "more-on-convex-functions"]], "More preprocessing": [[28, "more-preprocessing"], [30, "more-preprocessing"]], "Motivation for Adaptive Step Sizes": [[30, "motivation-for-adaptive-step-sizes"]], "Multilayer perceptrons": [[12, "multilayer-perceptrons"]], "Network requirements": [[2, "network-requirements"]], "Neural Networks vs CNNs": [[3, "neural-networks-vs-cnns"]], "Neural networks": [[12, null]], "Note about SVD Calculations": [[28, "note-about-svd-calculations"], [29, "note-about-svd-calculations"]], "Note on Scikit-Learn": [[29, "note-on-scikit-learn"]], "Numerical experiments and the covariance, central limit theorem": [[24, "numerical-experiments-and-the-covariance-central-limit-theorem"]], "Numpy and arrays": [[21, "numpy-and-arrays"], [27, 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"other-courses-on-data-science-and-machine-learning-at-uio"]], "Other courses on Data science and Machine Learning at UiO, contn": [[27, "other-courses-on-data-science-and-machine-learning-at-uio-contn"]], "Other popular texts": [[27, "other-popular-texts"]], "Other techniques": [[11, "other-techniques"]], "Other types of networks": [[12, "other-types-of-networks"]], "Other ways of visualizing the trees": [[9, "other-ways-of-visualizing-the-trees"]], "Our model for the nuclear binding energies": [[27, "our-model-for-the-nuclear-binding-energies"]], "Overview of first week": [[27, "overview-of-first-week"]], "Overview video on Stochastic Gradient Descent (SGD)": [[30, "overview-video-on-stochastic-gradient-descent-sgd"]], "Own code for Ordinary Least Squares": [[27, "own-code-for-ordinary-least-squares"], [28, "own-code-for-ordinary-least-squares"]], "PCA and scikit-learn": [[11, "pca-and-scikit-learn"]], "Pandas AI": [[27, "pandas-ai"]], "Part a : Ordinary Least Square (OLS) for the Runge function": [[22, "part-a-ordinary-least-square-ols-for-the-runge-function"]], "Part b: Adding Ridge regression for the Runge function": [[22, "part-b-adding-ridge-regression-for-the-runge-function"]], "Part c: Writing your own gradient descent code": [[22, "part-c-writing-your-own-gradient-descent-code"]], "Part d: Including momentum and more advanced ways to update the learning the rate": [[22, "part-d-including-momentum-and-more-advanced-ways-to-update-the-learning-the-rate"]], "Part e: Writing our own code for Lasso regression": [[22, "part-e-writing-our-own-code-for-lasso-regression"]], "Part f: Stochastic gradient descent": [[22, "part-f-stochastic-gradient-descent"]], "Part g: Bias-variance trade-off and resampling techniques": [[22, "part-g-bias-variance-trade-off-and-resampling-techniques"]], "Part h): Cross-validation as resampling techniques, adding more complexity": [[22, "part-h-cross-validation-as-resampling-techniques-adding-more-complexity"]], "Partial Differential 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overarching aims": [[27, "regression-analysis-overarching-aims"]], "Regression analysis, overarching aims II": [[27, "regression-analysis-overarching-aims-ii"]], "Regularization": [[1, "regularization"]], "Reminder from last week": [[28, "reminder-from-last-week"]], "Reminder on Newton-Raphson\u2019s method": [[29, "reminder-on-newton-raphson-s-method"]], "Reminder on Statistics": [[6, "reminder-on-statistics"]], "Reminder on different scaling methods": [[30, "reminder-on-different-scaling-methods"]], "Replace or not": [[13, "replace-or-not"], [30, "replace-or-not"]], "Required Technologies": [[20, "required-technologies"]], "Resampling Methods": [[6, null]], "Resampling and the Bias-Variance Trade-off": [[19, "resampling-and-the-bias-variance-trade-off"]], "Resampling methods": [[6, "id1"]], "Residual Error": [[28, "residual-error"], [29, "residual-error"]], "Resources on differential equations and deep learning": [[2, "resources-on-differential-equations-and-deep-learning"]], 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+++ b/doc/LectureNotes/_build/html/statistics.html
@@ -231,7 +231,7 @@
- Week 36: Linear Regression and Gradient descent
- Exercises week 37
- Week 37: Gradient descent methods -
- Exercises week 37 +
- Exercises week 38
Projects
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index 3cab094ed..1091bcdd8 100644
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@@ -229,7 +229,7 @@
- Week 36: Linear Regression and Gradient descent
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diff --git a/doc/LectureNotes/_build/html/textbooks.html b/doc/LectureNotes/_build/html/textbooks.html
index 841284c14..922abafd1 100644
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@@ -229,7 +229,7 @@
- Week 36: Linear Regression and Gradient descent
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diff --git a/doc/LectureNotes/_build/html/week34.html b/doc/LectureNotes/_build/html/week34.html
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@@ -231,7 +231,7 @@
- Week 36: Linear Regression and Gradient descent
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diff --git a/doc/LectureNotes/_build/html/week35.html b/doc/LectureNotes/_build/html/week35.html
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@@ -231,7 +231,7 @@
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diff --git a/doc/LectureNotes/_build/html/week36.html b/doc/LectureNotes/_build/html/week36.html
index a94cb62d1..3294e83c4 100644
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@@ -231,7 +231,7 @@
- Week 36: Linear Regression and Gradient descent
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diff --git a/doc/LectureNotes/_build/html/week37.html b/doc/LectureNotes/_build/html/week37.html
index a43f8c0cf..5545beb87 100644
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@@ -62,7 +62,7 @@
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@@ -2508,7 +2508,7 @@ centered matrix and/or vector that enter the fitting procedure.
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