FYS-STK4155/doc/LectureNotes/_build/html/searchindex.js

Search.setIndex({"alltitles": {"1a)": [[62, "a"]], "3-Clause BSD License": [[31, "clause-bsd-license"]], "3a)": [[62, "id1"]], "3b)": [[62, "b"]], "4a)": [[62, "id2"]], "4b)": [[62, "id3"]], "A Classification Tree": [[53, "a-classification-tree"]], "A Frequentist approach to data analysis": [[44, "a-frequentist-approach-to-data-analysis"], [73, "a-frequentist-approach-to-data-analysis"]], "A better approach": [[52, "a-better-approach"]], "A first summary": [[73, "a-first-summary"]], "A guide to masked arrays in NumPy": [[30, null]], "A more compact expression": [[78, "a-more-compact-expression"], [79, "a-more-compact-expression"]], "A new Cost Function": [[77, "a-new-cost-function"]], "A quick Reminder on Lagrangian Multipliers": [[52, "a-quick-reminder-on-lagrangian-multipliers"]], "A simple example": [[48, "a-simple-example"]], "A soft classifier": [[52, "a-soft-classifier"]], "A top-down perspective on Neural networks": [[45, "a-top-down-perspective-on-neural-networks"]], "A way to 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networks in Tensorflow and Keras": [[45, "building-neural-networks-in-tensorflow-and-keras"]], "But none of these can compete with Newton\u2019s method": [[76, "but-none-of-these-can-compete-with-newton-s-method"]], "CDN": [[29, "cdn"]], "CHANGELOG": [[29, "changelog"]], "CNNs in more detail, building convolutional neural networks in Tensorflow and Keras": [[47, "cnns-in-more-detail-building-convolutional-neural-networks-in-tensorflow-and-keras"]], "Cancer Data again now with Decision Trees and other Methods": [[53, "cancer-data-again-now-with-decision-trees-and-other-methods"]], "Chain rule": [[80, "chain-rule"]], "Chain rule, forward and reverse modes": [[80, "chain-rule-forward-and-reverse-modes"]], "Challenge: Choosing a Fixed Learning Rate": [[76, "challenge-choosing-a-fixed-learning-rate"]], "Choose cost function and optimizer": [[45, "choose-cost-function-and-optimizer"]], "Citations": [[22, "citations"]], "Class of functions we can approximate": [[80, 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"convolutional-neural-network"]], "Convolutional Neural Networks": [[47, null]], "Correlation Function and Design/Feature Matrix": [[74, "correlation-function-and-design-feature-matrix"]], "Correlation Matrix": [[55, "correlation-matrix"], [74, "correlation-matrix"]], "Correlation Matrix with Pandas": [[74, "correlation-matrix-with-pandas"]], "Counting the number of floating point operations": [[80, "counting-the-number-of-floating-point-operations"]], "Course Format": [[73, "course-format"]], "Course setting": [[69, null]], "Covariance Matrix Examples": [[74, "covariance-matrix-examples"]], "Covariance and Correlation Matrix": [[74, "covariance-and-correlation-matrix"]], "Create a notebook with MyST Markdown": [[23, "create-a-notebook-with-myst-markdown"]], "Creator": [[33, "creator"]], "Cross-validation": [[50, "cross-validation"]], "Cross-validation in brief": [[77, "cross-validation-in-brief"], [78, "cross-validation-in-brief"]], "Deadlines for projects (tentative)": [[73, "deadlines-for-projects-tentative"]], "Decision trees, overarching aims": [[53, null]], "Deep Neural Networks": [[76, "deep-neural-networks"]], "Deep learning methods": [[73, "deep-learning-methods"]], "Define model and architecture": [[45, "define-model-and-architecture"]], "Defining intermediate operations": [[80, "defining-intermediate-operations"]], "Defining the cost function": [[45, "defining-the-cost-function"]], "Definitions": [[63, "definitions"], [80, "definitions"]], "Deliverables": [[59, "deliverables"], [60, "deliverables"], [63, "deliverables"], [64, "deliverables"], [68, "deliverables"]], "Dependencies": [[29, "dependencies"]], "Derivation of the AdaGrad Algorithm": [[76, "derivation-of-the-adagrad-algorithm"]], "Derivative of the cost function": [[80, "derivative-of-the-cost-function"]], "Derivatives and the chain rule": [[56, "derivatives-and-the-chain-rule"], [80, "derivatives-and-the-chain-rule"]], "Derivatives in terms of z_j^L": [[80, "derivatives-in-terms-of-z-j-l"]], "Derivatives of the hidden layer": [[80, "derivatives-of-the-hidden-layer"]], "Derivatives, example 1": [[74, "derivatives-example-1"]], "Deriving OLS from a probability distribution": [[49, "deriving-ols-from-a-probability-distribution"], [77, "deriving-ols-from-a-probability-distribution"]], "Deriving and Implementing Ordinary Least Squares": [[60, "deriving-and-implementing-ordinary-least-squares"]], "Deriving and Implementing Ridge Regression": [[61, "deriving-and-implementing-ridge-regression"]], "Deriving the  Lasso Regression Equations": [[74, "deriving-the-lasso-regression-equations"], [75, "deriving-the-lasso-regression-equations"], [75, "id6"]], "Deriving the  Ridge Regression Equations": [[74, "deriving-the-ridge-regression-equations"], [75, "deriving-the-ridge-regression-equations"], [75, "id3"]], "Deriving the back propagation code for a multilayer perceptron model": [[56, "deriving-the-back-propagation-code-for-a-multilayer-perceptron-model"]], "Developing a code for doing neural networks with back propagation": [[45, "developing-a-code-for-doing-neural-networks-with-back-propagation"]], "Diagonalize the sample covariance matrix to obtain the principal components": [[55, "diagonalize-the-sample-covariance-matrix-to-obtain-the-principal-components"]], "Different kernels and Mercer\u2019s theorem": [[52, "different-kernels-and-mercer-s-theorem"]], "Disadvantages": [[53, "disadvantages"]], "Discriminative Modeling": [[73, "discriminative-modeling"]], "Discussing the correlation data": [[79, "discussing-the-correlation-data"]], "Does Logistic Regression do a better Job?": [[79, "does-logistic-regression-do-a-better-job"]], "Domains and probabilities": [[70, "domains-and-probabilities"]], "Dropout": [[45, "dropout"]], "Economy-size SVD": [[74, "economy-size-svd"], [75, "economy-size-svd"]], "Elements of Probability Theory and Statistical Data Analysis": [[70, null]], "Empirical Evidence: Convergence Time and Memory in Practice": [[76, "empirical-evidence-convergence-time-and-memory-in-practice"]], "Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods": [[54, null]], "Entropy and the ID3 algorithm": [[53, "entropy-and-the-id3-algorithm"]], "Essential elements of ML": [[73, "essential-elements-of-ml"]], "Evaluate model performance on test data": [[45, "evaluate-model-performance-on-test-data"]], "Example": [[27, "example"]], "Example 2": [[74, "example-2"]], "Example 3": [[74, "example-3"]], "Example 4": [[74, "example-4"]], "Example Matrix": [[74, "example-matrix"], [75, "example-matrix"]], "Example code for Bias-Variance tradeoff": [[77, "example-code-for-bias-variance-tradeoff"]], "Example code for Logistic Regression": [[78, "example-code-for-logistic-regression"], [79, "example-code-for-logistic-regression"]], "Example of discriminative modeling, taken from Generative Deep Learning by David Foster": [[73, "example-of-discriminative-modeling-taken-from-generative-deep-learning-by-david-foster"]], "Example of generative modeling, taken from Generative Deep Learning by David Foster": [[73, "example-of-generative-modeling-taken-from-generative-deep-learning-by-david-foster"]], "Example of own Standard scaling": [[74, "example-of-own-standard-scaling"]], "Example relevant for the exercises": [[74, "example-relevant-for-the-exercises"]], "Example: Exponential decay": [[46, "example-exponential-decay"]], "Example: Population growth": [[46, "example-population-growth"]], "Example: The diffusion equation": [[46, "example-the-diffusion-equation"]], "Example: binary classification problem": [[45, "example-binary-classification-problem"]], "Examples": [[73, "examples"]], "Examples of XOR, OR and AND gates": [[79, "examples-of-xor-or-and-and-gates"]], "Examples of likelihood functions used in logistic regression and neural networks": [[51, "examples-of-likelihood-functions-used-in-logistic-regression-and-neural-networks"]], "Examples of likelihood functions used in logistic regression and nueral networks": [[78, "examples-of-likelihood-functions-used-in-logistic-regression-and-nueral-networks"]], "Exercise 1": [[65, "exercise-1"]], "Exercise 1 - Choice of model and degrees of freedom": [[61, "exercise-1-choice-of-model-and-degrees-of-freedom"]], "Exercise 1 - Finding the derivative of Matrix-Vector expressions": [[60, "exercise-1-finding-the-derivative-of-matrix-vector-expressions"]], "Exercise 1 - Github Setup": [[59, "exercise-1-github-setup"]], "Exercise 1, scale your data": [[62, "exercise-1-scale-your-data"]], "Exercise 1: Creating the report document": [[64, "exercise-1-creating-the-report-document"]], "Exercise 1: Expectation values for ordinary least squares expressions": [[63, "exercise-1-expectation-values-for-ordinary-least-squares-expressions"]], "Exercise 1: Including more data": [[80, "exercise-1-including-more-data"]], "Exercise 1: Setting up various Python environments": [[44, "exercise-1-setting-up-various-python-environments"]], "Exercise 2": [[65, "exercise-2"]], "Exercise 2 - Deriving the expression for OLS": [[60, "exercise-2-deriving-the-expression-for-ols"]], "Exercise 2 - Deriving the expression for Ridge Regression": [[61, "exercise-2-deriving-the-expression-for-ridge-regression"]], "Exercise 2 - Setting up a Github repository": [[59, "exercise-2-setting-up-a-github-repository"]], "Exercise 2, calculate the gradients": [[62, "exercise-2-calculate-the-gradients"]], "Exercise 2: Adding good figures": [[64, "exercise-2-adding-good-figures"]], "Exercise 2: Expectation values for Ridge regression": [[63, "exercise-2-expectation-values-for-ridge-regression"]], "Exercise 2: Extended program": [[80, "exercise-2-extended-program"]], "Exercise 2: making your own data and exploring scikit-learn": [[44, "exercise-2-making-your-own-data-and-exploring-scikit-learn"]], "Exercise 3": [[65, "exercise-3"]], "Exercise 3 - Creating feature matrix and implementing OLS using the analytical expression": [[60, "exercise-3-creating-feature-matrix-and-implementing-ols-using-the-analytical-expression"]], "Exercise 3 - Fitting an OLS model to data": [[59, "exercise-3-fitting-an-ols-model-to-data"]], "Exercise 3 - Scaling data": [[61, "exercise-3-scaling-data"]], "Exercise 3 - Setting up a Python virtual environment": [[59, "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}": [[62, "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": [[63, "exercise-3-deriving-the-expression-for-the-bias-variance-trade-off"]], "Exercise 3: Normalizing our data": [[44, "exercise-3-normalizing-our-data"]], "Exercise 3: Writing an abstract and introduction": [[64, "exercise-3-writing-an-abstract-and-introduction"]], "Exercise 4 - Custom activation for each layer": [[65, "exercise-4-custom-activation-for-each-layer"]], "Exercise 4 - Fitting a polynomial": [[60, "exercise-4-fitting-a-polynomial"]], "Exercise 4 - Implementing Ridge Regression": [[61, "exercise-4-implementing-ridge-regression"]], "Exercise 4 - Testing multiple hyperparameters": [[61, "exercise-4-testing-multiple-hyperparameters"]], "Exercise 4 - The train-test split": [[59, "exercise-4-the-train-test-split"]], "Exercise 4, Implementing the simplest form for gradient descent": [[62, "exercise-4-implementing-the-simplest-form-for-gradient-descent"]], "Exercise 4: Adding Ridge Regression": [[44, "exercise-4-adding-ridge-regression"]], "Exercise 4: Computing the Bias and Variance": [[63, "exercise-4-computing-the-bias-and-variance"]], "Exercise 4: Making the code available and presentable": [[64, "exercise-4-making-the-code-available-and-presentable"]], "Exercise 5 - Comparing your code with sklearn": [[60, "exercise-5-comparing-your-code-with-sklearn"]], "Exercise 5 - Processing multiple inputs at once": [[65, "exercise-5-processing-multiple-inputs-at-once"]], "Exercise 5, Ridge regression and a new Synthetic Dataset": [[62, "exercise-5-ridge-regression-and-a-new-synthetic-dataset"]], "Exercise 5: Analytical exercises": [[44, "exercise-5-analytical-exercises"]], "Exercise 5: Interpretation of scaling and metrics": [[63, "exercise-5-interpretation-of-scaling-and-metrics"]], "Exercise 5: Referencing": [[64, "exercise-5-referencing"]], "Exercise 6 - Predicting on real data": [[65, "exercise-6-predicting-on-real-data"]], "Exercise 7 - Training on real data (Optional)": [[65, "exercise-7-training-on-real-data-optional"]], "Exercise:  Cross-validation as resampling techniques, adding more complexity": [[50, "exercise-cross-validation-as-resampling-techniques-adding-more-complexity"]], "Exercise: Analysis of real data": [[50, "exercise-analysis-of-real-data"]], "Exercise: Bias-variance trade-off and resampling techniques": [[50, "exercise-bias-variance-trade-off-and-resampling-techniques"]], "Exercise: Lasso Regression on the Franke function  with resampling": [[50, "exercise-lasso-regression-on-the-franke-function-with-resampling"]], "Exercise: Ordinary Least Square (OLS) on the Franke function": [[50, "exercise-ordinary-least-square-ols-on-the-franke-function"]], "Exercise: Ridge Regression on the Franke function  with resampling": [[50, "exercise-ridge-regression-on-the-franke-function-with-resampling"]], "Exercises": [[44, "exercises"]], "Exercises and Projects": [[50, "exercises-and-projects"]], "Exercises week 34": [[59, null]], "Exercises week 35": [[60, null]], "Exercises week 36": [[61, null]], "Exercises week 37": [[62, null]], "Exercises week 38": [[63, null]], "Exercises week 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"final-back-propagating-equation"]], "Final derivatives": [[80, "final-derivatives"]], "Final expression": [[80, "final-expression"]], "Final expressions for the biases of the hidden layer": [[80, "final-expressions-for-the-biases-of-the-hidden-layer"]], "Finding the Limit": [[77, "finding-the-limit"]], "Fine-tuning neural network hyperparameters": [[45, "fine-tuning-neural-network-hyperparameters"]], "First network example, simple percepetron with one input": [[80, "first-network-example-simple-percepetron-with-one-input"]], "Fitting an Equation of State for Dense Nuclear Matter": [[44, "fitting-an-equation-of-state-for-dense-nuclear-matter"]], "Fixing the singularity": [[74, "fixing-the-singularity"], [75, "fixing-the-singularity"]], "Format for electronic delivery of report and programs": [[68, "format-for-electronic-delivery-of-report-and-programs"]], "Forward and reverse modes": [[80, "forward-and-reverse-modes"]], "Frequently used scaling functions": [[74, "frequently-used-scaling-functions"], [76, "frequently-used-scaling-functions"]], "From OLS to Ridge and Lasso": [[75, "from-ols-to-ridge-and-lasso"]], "From one to many layers, the universal approximation theorem": [[56, "from-one-to-many-layers-the-universal-approximation-theorem"]], "Functionality in Scikit-Learn": [[74, "functionality-in-scikit-learn"], [76, "functionality-in-scikit-learn"]], "Further Dimensionality Remarks": [[47, "further-dimensionality-remarks"]], "Further properties (important for our analyses later)": [[49, "further-properties-important-for-our-analyses-later"], [74, "further-properties-important-for-our-analyses-later"], [75, "further-properties-important-for-our-analyses-later"]], "Gaussian Elimination": [[67, "gaussian-elimination"]], "General Features": [[53, "general-features"]], "General linear models and linear algebra": [[73, "general-linear-models-and-linear-algebra"]], "Generalizing the fitting procedure as a linear algebra problem": [[73, 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Classification Example": [[54, "gradient-boosting-classification-example"]], "Gradient Boosting, Examples of Regression": [[54, "gradient-boosting-examples-of-regression"]], "Gradient Clipping": [[45, "gradient-clipping"]], "Gradient Descent Example": [[75, "id1"], [76, "id1"]], "Gradient boosting: Basics with Steepest Descent/Functional Gradient Descent": [[54, "gradient-boosting-basics-with-steepest-descent-functional-gradient-descent"]], "Gradient descent": [[46, "gradient-descent"]], "Gradient descent and Ridge": [[75, "gradient-descent-and-ridge"], [76, "gradient-descent-and-ridge"]], "Gradient descent and revisiting Ordinary Least Squares from last week": [[76, "gradient-descent-and-revisiting-ordinary-least-squares-from-last-week"]], "Gradient descent example": [[75, "gradient-descent-example"], [76, "gradient-descent-example"]], "Gradient expressions": [[80, "gradient-expressions"]], "Grading": [[71, "grading"], [71, "id2"], [73, "grading"]], "Greative": [[14, null]], "History": [[30, "history"]], "How to take derivatives of Matrix-Vector expressions": [[60, "how-to-take-derivatives-of-matrix-vector-expressions"]], "Hyperplanes and all that": [[52, "hyperplanes-and-all-that"]], "Identifying Terms": [[77, "identifying-terms"]], "Illustration of a single perceptron model and a multi-perceptron model": [[79, "illustration-of-a-single-perceptron-model-and-a-multi-perceptron-model"], [80, "illustration-of-a-single-perceptron-model-and-a-multi-perceptron-model"]], "Important Matrix and vector handling packages": [[67, "important-matrix-and-vector-handling-packages"]], "Important observations": [[80, "important-observations"]], "Important technicalities: More on Rescaling data": [[74, "important-technicalities-more-on-rescaling-data"]], "Improving gradient descent with momentum": [[76, "improving-gradient-descent-with-momentum"]], "Improving performance": [[45, "improving-performance"]], "In general not this simple": [[80, "in-general-not-this-simple"]], "In summary": [[71, "in-summary"]], "Including Stochastic Gradient Descent with Autograd": [[57, "including-stochastic-gradient-descent-with-autograd"], [76, "including-stochastic-gradient-descent-with-autograd"]], "Including more classes": [[78, "including-more-classes"], [79, "including-more-classes"]], "Incremental PCA": [[55, "incremental-pca"]], "Independent and Identically Distributed (iid)": [[77, "independent-and-identically-distributed-iid"]], "Inputs to the activation function": [[80, "inputs-to-the-activation-function"]], "Install": [[28, "install"]], "Installation": [[27, "installation"]], "Installing R, C++, cython or Julia": [[73, "installing-r-c-cython-or-julia"]], "Installing R, C++, cython, Numba etc": [[73, "installing-r-c-cython-numba-etc"]], "Instructor information": [[71, "instructor-information"]], "Interpretations and optimizing our parameters": [[73, "interpretations-and-optimizing-our-parameters"], [73, "id2"], [73, "id3"], [74, "interpretations-and-optimizing-our-parameters"], [74, "id1"], [74, "id2"]], "Interpreting the Ridge results": [[74, "interpreting-the-ridge-results"], [75, "interpreting-the-ridge-results"], [75, "id4"]], "Introducing JAX": [[57, "introducing-jax"]], "Introducing the Covariance and Correlation functions": [[55, "introducing-the-covariance-and-correlation-functions"], [74, "introducing-the-covariance-and-correlation-functions"]], "Introduction": [[44, "introduction"], [50, "introduction"], [66, "introduction"], [67, "introduction"]], "Introduction to Neural networks": [[79, "introduction-to-neural-networks"], [80, "introduction-to-neural-networks"]], "Introduction to numerical projects": [[68, "introduction-to-numerical-projects"]], "Iterative Fitting, Classification and AdaBoost": [[54, "iterative-fitting-classification-and-adaboost"]], "Iterative Fitting, Regression and Squared-error Cost Function": [[54, "iterative-fitting-regression-and-squared-error-cost-function"]], "Kernel PCA": [[55, "kernel-pca"]], "Kernels and non-linearity": [[52, "kernels-and-non-linearity"]], "LU Decomposition, the inverse of a matrix": [[67, "lu-decomposition-the-inverse-of-a-matrix"]], "Lab sessions Tuesday and Wednesday": [[79, "lab-sessions-tuesday-and-wednesday"]], "Lab sessions on Tuesday and Wednesday": [[80, "lab-sessions-on-tuesday-and-wednesday"]], "Lab sessions week 39": [[78, "lab-sessions-week-39"]], "Lasso Regression": [[75, "lasso-regression"]], "Lasso case": [[75, "lasso-case"]], "Layers": [[45, "layers"]], "Layers used to build CNNs": [[47, "layers-used-to-build-cnns"]], "Layout of a neural network with three hidden layers": [[80, "layout-of-a-neural-network-with-three-hidden-layers"]], "Layout of a simple neural network with no hidden layer": [[80, "layout-of-a-simple-neural-network-with-no-hidden-layer"]], "Layout of a simple neural network with one hidden layer": [[80, "layout-of-a-simple-neural-network-with-one-hidden-layer"]], "Layout of a simple neural network with two input nodes, one  hidden layer and one output node": [[80, "layout-of-a-simple-neural-network-with-two-input-nodes-one-hidden-layer-and-one-output-node"]], "Learn more": [[22, "learn-more"]], "Learning goals": [[59, "learning-goals"], [60, "learning-goals"], [61, "learning-goals"], [62, "learning-goals"], [63, "learning-goals"], [64, "learning-goals"]], "Learning outcomes": [[66, "learning-outcomes"], [73, "learning-outcomes"]], "Lecture Monday  October 6": [[80, "lecture-monday-october-6"]], "Lecture Monday September 29, 2025": [[79, "lecture-monday-september-29-2025"]], "Lecture material": [[78, "lecture-material"]], "Lectures and ComputerLab": [[73, "lectures-and-computerlab"]], "License": [[27, "license"], [28, "license"], [29, "license"]], "License for Sphinx": [[37, null]], "Licenses for incorporated software": [[37, "licenses-for-incorporated-software"]], "Limitations of supervised learning with deep networks": [[45, "limitations-of-supervised-learning-with-deep-networks"]], "Linear Algebra, Handling of Arrays and more Python Features": [[67, null]], "Linear Regression": [[44, null]], "Linear Regression Problems": [[74, "linear-regression-problems"], [75, "linear-regression-problems"]], "Linear Regression and  the SVD": [[75, "linear-regression-and-the-svd"]], "Linear Regression, basic elements": [[44, "linear-regression-basic-elements"]], "Linear classifier": [[78, "linear-classifier"]], "Linking Bayes\u2019 Theorem with Ridge and Lasso Regression": [[49, "linking-bayes-theorem-with-ridge-and-lasso-regression"]], "Linking the regression analysis with a statistical interpretation": [[49, "linking-the-regression-analysis-with-a-statistical-interpretation"], [77, "linking-the-regression-analysis-with-a-statistical-interpretation"]], "Linking with the SVD": [[49, "linking-with-the-svd"], [74, "linking-with-the-svd"]], "Links to relevant courses at the University of Oslo": [[72, "links-to-relevant-courses-at-the-university-of-oslo"]], "Logistic Regression": [[51, null], [51, "id1"], [78, "logistic-regression"]], "Logistic Regression, from last week": [[79, "logistic-regression-from-last-week"]], "MNIST and GANs": [[48, "mnist-and-gans"]], "Machine Learning": [[73, "machine-learning"]], "Machine learning": [[66, "machine-learning"]], "Main differences": [[30, "main-differences"]], "Main textbooks": [[73, "main-textbooks"]], "Making a tree": [[53, "making-a-tree"]], "Making your own Bootstrap: Changing the Level of the Decision Tree": [[54, "making-your-own-bootstrap-changing-the-level-of-the-decision-tree"]], "Making your own test-train splitting": [[74, "making-your-own-test-train-splitting"]], "Markdown + notebooks": [[24, "markdown-notebooks"]], "Markdown Files": [[22, null]], "Masked records": [[30, "masked-records"]], "Material for exercises week 35": [[74, "material-for-exercises-week-35"]], "Material for lab sessions  sessions Tuesday and Wednesday": [[75, "material-for-lab-sessions-sessions-tuesday-and-wednesday"]], "Material for lecture Monday September 2": [[75, "material-for-lecture-monday-september-2"]], "Material for lecture Monday September 8": [[76, "material-for-lecture-monday-september-8"]], "Material for the lab sessions": [[76, "material-for-the-lab-sessions"], [77, "material-for-the-lab-sessions"]], "Material for the lecture on Monday October 6, 2025": [[80, "material-for-the-lecture-on-monday-october-6-2025"]], "Mathematical Interpretation of Ordinary Least Squares": [[49, "mathematical-interpretation-of-ordinary-least-squares"], [74, "mathematical-interpretation-of-ordinary-least-squares"], [75, "mathematical-interpretation-of-ordinary-least-squares"]], "Mathematical model": [[79, "mathematical-model"], [79, "id1"], [79, "id2"], [79, "id3"], [79, "id4"]], "Mathematical optimization of convex functions": [[52, "mathematical-optimization-of-convex-functions"]], "Mathematics of CNNs": [[47, "mathematics-of-cnns"]], "Mathematics of deep learning": [[80, "mathematics-of-deep-learning"]], "Mathematics of deep learning and neural networks": [[80, "mathematics-of-deep-learning-and-neural-networks"]], "Mathematics of the SVD and implications": [[49, "mathematics-of-the-svd-and-implications"], [74, "mathematics-of-the-svd-and-implications"], [75, "mathematics-of-the-svd-and-implications"]], "Matrices in Python": [[73, "matrices-in-python"]], "Matrix  multiplication": [[45, "matrix-multiplication"]], "Matrix-vector notation": [[79, "matrix-vector-notation"]], "Matrix-vector notation  and activation": [[56, "matrix-vector-notation-and-activation"], [79, "matrix-vector-notation-and-activation"]], "Maximum Likelihood Estimation (MLE)": [[77, "maximum-likelihood-estimation-mle"]], "Maximum likelihood": [[78, "maximum-likelihood"], [79, "maximum-likelihood"]], "Meet the  covariance!": [[70, "meet-the-covariance"]], "Meet the Covariance Matrix": [[49, "meet-the-covariance-matrix"], [74, "meet-the-covariance-matrix"]], "Meet the Hessian Matrix": [[74, "meet-the-hessian-matrix"]], "Meet the Pandas": [[73, "meet-the-pandas"]], "Memory Usage and Scalability": [[76, "memory-usage-and-scalability"]], "Memory constraints": [[76, "memory-constraints"]], "Min-Max Scaling": [[74, "min-max-scaling"]], "Minimizing the cross entropy": [[78, "minimizing-the-cross-entropy"], [79, "minimizing-the-cross-entropy"]], "Momentum based GD": [[57, "momentum-based-gd"], [76, "momentum-based-gd"]], "More classes": [[78, "more-classes"], [79, "more-classes"]], "More complicated Example: The Ising model": [[50, "more-complicated-example-the-ising-model"]], "More complicated function": [[80, "more-complicated-function"]], "More considerations": [[80, "more-considerations"]], "More examples on bootstrap and cross-validation and errors": [[77, "more-examples-on-bootstrap-and-cross-validation-and-errors"], [78, "more-examples-on-bootstrap-and-cross-validation-and-errors"]], "More interpretations": [[74, "more-interpretations"], [75, "more-interpretations"], [75, "id5"]], "More on Dimensionalities": [[47, "more-on-dimensionalities"]], "More on Rescaling data": [[50, "more-on-rescaling-data"]], "More on Steepest descent": [[75, "more-on-steepest-descent"]], "More on convex functions": [[75, "more-on-convex-functions"]], "More on the general approximation theorem": [[80, "more-on-the-general-approximation-theorem"]], "More preprocessing": [[74, "more-preprocessing"], [76, "more-preprocessing"]], "Motivation for Adaptive Step Sizes": [[76, "motivation-for-adaptive-step-sizes"]], "Multilayer perceptrons": [[56, "multilayer-perceptrons"], [79, "multilayer-perceptrons"], [80, "multilayer-perceptrons"]], "Multivariable functions": [[80, "multivariable-functions"]], "MyST markdown": [[24, "myst-markdown"]], "NCSA Open Source License": [[31, null]], "Network requirements": [[46, "network-requirements"]], "Neural Networks vs CNNs": [[47, "neural-networks-vs-cnns"]], "Neural network types": [[79, "neural-network-types"], [80, "neural-network-types"]], "Neural networks": [[56, null]], "New expression for the derivative": [[80, "new-expression-for-the-derivative"]], "New features": [[30, "new-features"]], "Non-Convex Problems": [[76, "non-convex-problems"]], "Note about SVD Calculations": [[74, "note-about-svd-calculations"], [75, "note-about-svd-calculations"]], "Note on Scikit-Learn": [[75, "note-on-scikit-learn"]], "Notebooks with MyST Markdown": [[23, null]], "Numerical experiments and the covariance, central limit theorem": [[70, "numerical-experiments-and-the-covariance-central-limit-theorem"]], "Numpy and arrays": [[67, "numpy-and-arrays"], [73, "numpy-and-arrays"]], "Numpy examples and Important Matrix and vector handling packages": [[73, "numpy-examples-and-important-matrix-and-vector-handling-packages"]], "Optimization and Deep learning": [[78, "optimization-and-deep-learning"], [79, "optimization-and-deep-learning"]], "Optimization and gradient descent, the central part of any Machine Learning algortithm": [[75, "optimization-and-gradient-descent-the-central-part-of-any-machine-learning-algortithm"]], "Optimization, the central part of any Machine Learning algortithm": [[57, null], [78, "optimization-the-central-part-of-any-machine-learning-algortithm"], [79, "optimization-the-central-part-of-any-machine-learning-algortithm"]], "Optimizing maskedarray": [[30, "optimizing-maskedarray"]], "Optimizing our parameters": [[73, "optimizing-our-parameters"]], "Optimizing our parameters, more details": [[73, "optimizing-our-parameters-more-details"]], "Optimizing the cost function": [[45, "optimizing-the-cost-function"]], "Optimizing the parameters": [[80, "optimizing-the-parameters"]], "Organizing our data": [[44, "organizing-our-data"], [73, "organizing-our-data"]], "Other Matrix and Vector Operations": [[67, "other-matrix-and-vector-operations"]], "Other Types of Recurrent Neural Networks": [[48, "other-types-of-recurrent-neural-networks"]], "Other courses on Data science and Machine Learning  at UiO": [[73, "other-courses-on-data-science-and-machine-learning-at-uio"]], "Other courses on Data science and Machine Learning  at UiO, contn": [[73, "other-courses-on-data-science-and-machine-learning-at-uio-contn"]], "Other ingredients of a neural network": [[80, "other-ingredients-of-a-neural-network"]], "Other measures in classification studies": [[79, "other-measures-in-classification-studies"]], "Other parameters": [[80, "other-parameters"]], "Other popular texts": [[73, "other-popular-texts"]], "Other techniques": [[55, "other-techniques"]], "Other types of networks": [[56, "other-types-of-networks"], [79, "other-types-of-networks"], [80, "other-types-of-networks"]], "Other ways of visualizing the trees": [[53, "other-ways-of-visualizing-the-trees"]], "Our Copyright Policy": [[20, "our-copyright-policy"]], "Our model for the nuclear binding energies": [[73, "our-model-for-the-nuclear-binding-energies"]], "Output layer": [[80, "output-layer"]], "Overarching aims of the exercises this week": [[65, "overarching-aims-of-the-exercises-this-week"]], "Overarching view of a neural network": [[80, "overarching-view-of-a-neural-network"]], "Overview of first week": [[73, "overview-of-first-week"]], "Overview video on Stochastic Gradient Descent (SGD)": [[76, "overview-video-on-stochastic-gradient-descent-sgd"]], "Own code for Ordinary Least Squares": [[73, "own-code-for-ordinary-least-squares"], [74, "own-code-for-ordinary-least-squares"]], "PCA and scikit-learn": [[55, "pca-and-scikit-learn"]], "Pandas AI": [[73, "pandas-ai"]], "Parameters of neural networks": [[80, "parameters-of-neural-networks"]], "Part a : Ordinary Least Square (OLS) for the Runge function": [[68, "part-a-ordinary-least-square-ols-for-the-runge-function"]], "Part b: Adding Ridge regression for the Runge function": [[68, "part-b-adding-ridge-regression-for-the-runge-function"]], "Part c: Writing your own gradient descent code": [[68, "part-c-writing-your-own-gradient-descent-code"]], "Part d: Including momentum and more advanced ways to update the learning the rate": [[68, "part-d-including-momentum-and-more-advanced-ways-to-update-the-learning-the-rate"]], "Part e: Writing our own code for Lasso regression": [[68, "part-e-writing-our-own-code-for-lasso-regression"]], "Part f: Stochastic gradient descent": [[68, "part-f-stochastic-gradient-descent"]], "Part g: Bias-variance trade-off and resampling techniques": [[68, "part-g-bias-variance-trade-off-and-resampling-techniques"]], "Part h): Cross-validation as resampling techniques, adding more complexity": [[68, "part-h-cross-validation-as-resampling-techniques-adding-more-complexity"]], "Partial Differential Equations": [[46, "partial-differential-equations"]], "Pitaya Smoothie": [[15, null]], "Plan for week 39, September 22-26, 2025": [[78, "plan-for-week-39-september-22-26-2025"]], "Plan for week 41, October 6-10": [[80, "plan-for-week-41-october-6-10"]], "Plans for week 35": [[74, "plans-for-week-35"]], "Plans for week 36": [[75, "plans-for-week-36"]], "Plans for week 37, lecture Monday": [[76, "plans-for-week-37-lecture-monday"]], "Plans for week 38, lecture Monday September 15": [[77, "plans-for-week-38-lecture-monday-september-15"]], "Plotting the Histogram": [[77, "plotting-the-histogram"]], "Plotting the mean value for each group": [[78, "plotting-the-mean-value-for-each-group"]], "Practical tips": [[57, "practical-tips"], [76, "practical-tips"]], "Practicalities": [[71, "practicalities"], [71, "id1"]], "Preamble: Note on writing reports, using reference material, AI and other tools": [[68, "preamble-note-on-writing-reports-using-reference-material-ai-and-other-tools"]], "Predicting New Points With A Trained Recurrent Neural Network": [[48, "predicting-new-points-with-a-trained-recurrent-neural-network"]], "Preprocessing our data": [[74, "preprocessing-our-data"]], "Prerequisites": [[73, "prerequisites"]], "Prerequisites and background": [[66, "prerequisites-and-background"]], "Prerequisites: Collect and pre-process data": [[47, "prerequisites-collect-and-pre-process-data"]], "Probability Distribution Functions": [[70, "probability-distribution-functions"]], "Program example for gradient descent with Ridge Regression": [[75, "program-example-for-gradient-descent-with-ridge-regression"], [76, "program-example-for-gradient-descent-with-ridge-regression"]], "Program for stochastic gradient": [[57, "program-for-stochastic-gradient"]], "Project 1 on Machine Learning, deadline October 6 (midnight), 2025": [[68, null]], "Properties of PDFs": [[70, "properties-of-pdfs"]], "Pros and cons": [[76, "pros-and-cons"]], "Pros and cons of trees, pros": [[53, "pros-and-cons-of-trees-pros"]], "Python installers": [[66, "python-installers"], [73, "python-installers"]], "Quickly add YAML metadata for MyST Notebooks": [[23, "quickly-add-yaml-metadata-for-myst-notebooks"]], "RMS prop": [[57, "rms-prop"]], "RMSProp algorithm, taken from Goodfellow et al": [[76, "rmsprop-algorithm-taken-from-goodfellow-et-al"]], "RMSProp: Adaptive Learning Rates": [[76, "rmsprop-adaptive-learning-rates"]], "RMSprop for adaptive learning rate with Stochastic Gradient Descent": [[76, "rmsprop-for-adaptive-learning-rate-with-stochastic-gradient-descent"]], "Random Numbers": [[70, "random-numbers"]], "Random forests": [[54, "random-forests"]], "Randomized PCA": [[55, "randomized-pca"]], "Reading material": [[73, "reading-material"]], "Reading recommendations:": [[74, "reading-recommendations"]], "Reading suggestions week 34": [[73, "reading-suggestions-week-34"]], "Readings and Videos": [[77, "readings-and-videos"]], "Readings and Videos, logistic regression": [[78, "readings-and-videos-logistic-regression"]], "Readings and Videos, resampling methods": [[78, "readings-and-videos-resampling-methods"]], "Readings and Videos:": [[76, "readings-and-videos"], [80, "readings-and-videos"]], "Recurrent neural networks": [[56, "recurrent-neural-networks"], [79, "recurrent-neural-networks"], [80, "recurrent-neural-networks"]], "Recurrent neural networks: Overarching view": [[48, null]], "Reducing the number of degrees of freedom, overarching view": [[44, "reducing-the-number-of-degrees-of-freedom-overarching-view"], [74, "reducing-the-number-of-degrees-of-freedom-overarching-view"]], "Reducing the number of operations": [[80, "reducing-the-number-of-operations"]], "Reformulating the problem": [[46, "reformulating-the-problem"]], "Regression Case": [[54, "regression-case"]], "Regression analysis and resampling methods": [[68, "regression-analysis-and-resampling-methods"]], "Regression analysis, overarching aims": [[73, "regression-analysis-overarching-aims"]], "Regression analysis, overarching aims II": [[73, "regression-analysis-overarching-aims-ii"]], "Regularization": [[45, "regularization"]], "Relevance": [[79, "relevance"]], "Reminder from last week": [[74, "reminder-from-last-week"]], "Reminder on Newton-Raphson\u2019s method": [[75, "reminder-on-newton-raphson-s-method"]], "Reminder on Statistics": [[50, "reminder-on-statistics"]], "Reminder on books with hands-on material and codes": [[80, "reminder-on-books-with-hands-on-material-and-codes"]], "Reminder on different scaling methods": [[76, "reminder-on-different-scaling-methods"]], "Reminder on the chain rule and gradients": [[80, "reminder-on-the-chain-rule-and-gradients"]], "Replace or not": [[57, "replace-or-not"], [76, "replace-or-not"]], "Required Technologies": [[66, "required-technologies"]], "Resampling Methods": [[50, null]], "Resampling and the Bias-Variance Trade-off": [[63, "resampling-and-the-bias-variance-trade-off"]], "Resampling approaches can be computationally expensive": [[77, "resampling-approaches-can-be-computationally-expensive"], [78, "resampling-approaches-can-be-computationally-expensive"]], "Resampling methods": [[50, "id1"], [77, "resampling-methods"], [77, "id2"], [78, "resampling-methods"], [78, "id1"]], "Resampling methods: Bootstrap": [[77, "resampling-methods-bootstrap"], [78, "resampling-methods-bootstrap"]], "Resampling methods: Bootstrap approach": [[77, "resampling-methods-bootstrap-approach"]], "Resampling methods: Bootstrap background": [[77, "resampling-methods-bootstrap-background"]], "Resampling methods: Bootstrap steps": [[77, "resampling-methods-bootstrap-steps"]], "Resampling methods: More Bootstrap background": [[77, "resampling-methods-more-bootstrap-background"]], "Residual Error": [[74, "residual-error"], [75, "residual-error"]], "Resources on differential equations and deep learning": [[46, "resources-on-differential-equations-and-deep-learning"]], "Revision notes": [[30, "revision-notes"]], "Revisiting Ordinary Least Squares": [[75, "revisiting-ordinary-least-squares"]], "Revisiting our Linear Regression Solvers": [[57, "revisiting-our-linear-regression-solvers"]], "Revisiting our Logistic Regression case": [[78, "revisiting-our-logistic-regression-case"], [79, "revisiting-our-logistic-regression-case"]], "Rewriting the Covariance and/or Correlation Matrix": [[74, "rewriting-the-covariance-and-or-correlation-matrix"]], "Rewriting the \\delta-function": [[77, "rewriting-the-delta-function"]], "Rewriting the fitting procedure as a linear algebra problem": [[73, "rewriting-the-fitting-procedure-as-a-linear-algebra-problem"]], "Rewriting the fitting procedure as a linear algebra problem, more details": [[73, "rewriting-the-fitting-procedure-as-a-linear-algebra-problem-more-details"]], "Ridge Regression": [[75, "ridge-regression"]], "Ridge and LASSO Regression": [[74, "ridge-and-lasso-regression"], [75, "ridge-and-lasso-regression"], [75, "id2"]], "Ridge and Lasso Regression": [[49, null], [49, "id1"]], "SGD example": [[76, "sgd-example"]], "SGD vs Full-Batch GD: Convergence Speed and Memory Comparison": [[76, "sgd-vs-full-batch-gd-convergence-speed-and-memory-comparison"]], "SVD analysis": [[75, "svd-analysis"]], "Same code but now with momentum gradient descent": [[57, "same-code-but-now-with-momentum-gradient-descent"], [76, "same-code-but-now-with-momentum-gradient-descent"], [76, "id3"], [76, "id4"]], "Sample Roles and Directives": [[22, "sample-roles-and-directives"]], "Schedule first week": [[73, "schedule-first-week"]], "Schematic Regression Procedure": [[53, "schematic-regression-procedure"]], "Second moment of the gradient": [[76, "second-moment-of-the-gradient"]], "September 15-19": [[63, "september-15-19"]], "Setting up the Back propagation algorithm": [[56, "setting-up-the-back-propagation-algorithm"]], "Setting up the Back propagation algorithm, part 3": [[80, "setting-up-the-back-propagation-algorithm-part-3"]], "Setting up the Matrix to be inverted": [[74, "setting-up-the-matrix-to-be-inverted"], [75, "setting-up-the-matrix-to-be-inverted"]], "Setting up the back propagation algorithm": [[80, "setting-up-the-back-propagation-algorithm"]], "Setting up the back propagation algorithm, part 2": [[80, "setting-up-the-back-propagation-algorithm-part-2"]], "Setting up the equations for a neural network": [[80, "setting-up-the-equations-for-a-neural-network"]], "Setting up the network using Autograd; The full program": [[46, "setting-up-the-network-using-autograd-the-full-program"]], "Should masked arrays be filled before processing or not?": [[30, "should-masked-arrays-be-filled-before-processing-or-not"]], "Show me": [[29, "show-me"]], "Similar (second order function now) problem but now with AdaGrad": [[57, "similar-second-order-function-now-problem-but-now-with-adagrad"], [76, "similar-second-order-function-now-problem-but-now-with-adagrad"]], "Simple Python Code to read in Data and perform Classification": [[53, "simple-python-code-to-read-in-data-and-perform-classification"]], "Simple case": [[74, "simple-case"], [75, "simple-case"]], "Simple code for solving the above problem": [[75, "simple-code-for-solving-the-above-problem"]], "Simple example": [[78, "simple-example"], [80, "simple-example"]], "Simple example code": [[76, "simple-example-code"]], "Simple example to illustrate Ordinary Least Squares, Ridge and Lasso Regression": [[75, "simple-example-to-illustrate-ordinary-least-squares-ridge-and-lasso-regression"]], "Simple geometric interpretation": [[75, "simple-geometric-interpretation"]], "Simple linear regression model using scikit-learn": [[44, "simple-linear-regression-model-using-scikit-learn"], [73, "simple-linear-regression-model-using-scikit-learn"]], "Simple neural network and the  back propagation equations": [[80, "simple-neural-network-and-the-back-propagation-equations"]], "Simple one-dimensional second-order polynomial": [[62, "simple-one-dimensional-second-order-polynomial"]], "Simple program": [[75, "simple-program"], [76, "simple-program"]], "Simpler examples first, and automatic differentiation": [[80, "simpler-examples-first-and-automatic-differentiation"]], "Slightly different approach": [[76, "slightly-different-approach"]], "Smarter way of evaluating the above function": [[80, "smarter-way-of-evaluating-the-above-function"]], "Sneaking in automatic differentiation using Autograd": [[76, "sneaking-in-automatic-differentiation-using-autograd"]], "Software and needed installations": [[68, "software-and-needed-installations"], [73, "software-and-needed-installations"]], "Solving Differential Equations  with Deep Learning": [[46, null]], "Solving the one dimensional Poisson equation": [[46, "solving-the-one-dimensional-poisson-equation"]], "Solving the wave equation with Neural Networks": [[46, "solving-the-wave-equation-with-neural-networks"]], "Solving using Newton-Raphson\u2019s method": [[78, "solving-using-newton-raphson-s-method"], [79, "solving-using-newton-raphson-s-method"]], "Some famous Matrices": [[67, "some-famous-matrices"]], "Some parallels from real analysis": [[80, "some-parallels-from-real-analysis"]], "Some selected properties": [[78, "some-selected-properties"]], "Some simple problems": 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"stochastic-gradient-descent-sgd"]], "Stochastic variables and the main concepts, the discrete case": [[70, "stochastic-variables-and-the-main-concepts-the-discrete-case"]], "Strongly Convex Case": [[76, "strongly-convex-case"]], "Structure of translation files": [[41, "structure-of-translation-files"]], "Suggested readings and videos": [[79, "suggested-readings-and-videos"]], "Summing up": [[77, "summing-up"], [78, "summing-up"]], "Support Vector Machines, overarching aims": [[52, null]], "Synthetic data generation": [[78, "synthetic-data-generation"], [79, "synthetic-data-generation"]], "Systematic reduction": [[47, "systematic-reduction"]], "Teachers": [[73, "teachers"]], "Teachers and Grading": [[71, null]], "Teaching Assistants Fall semester 2023": [[71, "teaching-assistants-fall-semester-2023"]], "Tentative deadllines for projects": [[71, "tentative-deadllines-for-projects"]], "Testing the Means Squared Error as function of Complexity": [[44, "testing-the-means-squared-error-as-function-of-complexity"], [74, "testing-the-means-squared-error-as-function-of-complexity"]], "Textbooks": [[72, null]], "Thanks": [[30, "thanks"]], "The Algorithm before theorem": [[55, "the-algorithm-before-theorem"]], "The Breast Cancer Data, now with Keras": [[45, "the-breast-cancer-data-now-with-keras"]], "The CART algorithm for Classification": [[53, "the-cart-algorithm-for-classification"]], "The CART algorithm for Regression": [[53, "the-cart-algorithm-for-regression"]], "The CIFAR01 data set": [[47, "the-cifar01-data-set"]], "The Central Limit Theorem": [[77, "the-central-limit-theorem"]], "The Hessian matrix": [[75, "the-hessian-matrix"], [76, "the-hessian-matrix"]], "The Hessian matrix for Ridge Regression": [[75, "the-hessian-matrix-for-ridge-regression"], [76, "the-hessian-matrix-for-ridge-regression"]], "The IPython licensing terms": [[20, null]], "The Jacobian": [[74, "the-jacobian"]], "The MIT License (MIT)": [[19, null]], "The MNIST 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Dark", "Gotthard Light", "Greative", "Pitaya Smoothie", "&lt;no title&gt;", "&lt;no title&gt;", "&lt;no title&gt;", "The MIT License (MIT)", "The IPython licensing terms", "Welcome to your Jupyter Book", "Markdown Files", "Notebooks with MyST Markdown", "Content with notebooks", "&lt;no title&gt;", "&lt;no title&gt;", "markdown-it-container", "markdown-it-deflist", "markdown-it-texmath", "A guide to masked arrays in NumPy", "NCSA Open Source License", "&lt;no title&gt;", "Authors", "&lt;no title&gt;", "&lt;no title&gt;", "&lt;no title&gt;", "License for Sphinx", "&lt;no title&gt;", "&lt;no title&gt;", "&lt;no title&gt;", "Translation workflow", "&lt;no title&gt;", "&lt;no title&gt;", "<span class=\"section-number\">3. </span>Linear Regression", "<span class=\"section-number\">14. </span>Building a Feed Forward Neural Network", "<span class=\"section-number\">15. </span>Solving Differential Equations  with Deep Learning", "<span class=\"section-number\">16. </span>Convolutional Neural Networks", "<span class=\"section-number\">17. </span>Recurrent neural networks: Overarching view", "<span class=\"section-number\">4. </span>Ridge and Lasso Regression", "<span class=\"section-number\">5. </span>Resampling Methods", "<span class=\"section-number\">6. </span>Logistic Regression", "<span class=\"section-number\">8. </span>Support Vector Machines, overarching aims", "<span class=\"section-number\">9. </span>Decision trees, overarching aims", "<span class=\"section-number\">10. </span>Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods", "<span class=\"section-number\">11. </span>Basic ideas of the Principal Component Analysis (PCA)", "<span class=\"section-number\">13. </span>Neural networks", "<span class=\"section-number\">7. </span>Optimization, the central part of any Machine Learning algortithm", "<span class=\"section-number\">12. </span>Clustering and Unsupervised Learning", "Exercises week 34", "Exercises week 35", "Exercises week 36", "Exercises week 37", "Exercises week 38", "Exercises week 39", "Exercises week 41", "Applied Data Analysis and Machine Learning", "<span class=\"section-number\">2. </span>Linear Algebra, Handling of Arrays and more Python Features", "Project 1 on Machine Learning, deadline October 6 (midnight), 2025", "Course setting", "<span class=\"section-number\">1. </span>Elements of Probability Theory and Statistical Data Analysis", "Teachers and Grading", "Textbooks", "Week 34: Introduction to the course, Logistics and Practicalities", "Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression", "Week 36: Linear Regression and Gradient descent", "Week 37: Gradient descent methods", "Week 38: Statistical analysis, bias-variance tradeoff and resampling methods", "Week 39: Resampling methods and logistic regression", "Week 40: Gradient descent methods (continued) and start Neural networks", "Week 41 Neural networks and constructing a neural network code"], 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