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

Search.setIndex({"alltitles": {"A Classification Tree": [[10, "a-classification-tree"]], "A Frequentist approach to data analysis": [[1, "a-frequentist-approach-to-data-analysis"], [23, "a-frequentist-approach-to-data-analysis"]], "A better approach": [[9, "a-better-approach"]], "A first summary": [[23, "a-first-summary"]], "A more compact expression": [[0, "a-more-compact-expression"]], "A quick Reminder on Lagrangian Multipliers": [[9, "a-quick-reminder-on-lagrangian-multipliers"]], "A simple example": [[5, "a-simple-example"]], "A soft classifier": [[9, "a-soft-classifier"]], "A top-down perspective on Neural networks": [[2, "a-top-down-perspective-on-neural-networks"]], "ADAM optimizer": [[14, "adam-optimizer"]], "Activation functions": [[13, "activation-functions"]], "Adaptive boosting: AdaBoost, Basic Algorithm": [[11, "adaptive-boosting-adaboost-basic-algorithm"]], "Adding error analysis and training set up": [[23, "adding-error-analysis-and-training-set-up"]], "Adjust hyperparameters": [[2, "adjust-hyperparameters"]], "Algorithms for Setting up Decision Trees": [[10, "algorithms-for-setting-up-decision-trees"]], "An Overview of Ensemble Methods": [[11, "an-overview-of-ensemble-methods"]], "An extrapolation example": [[5, "an-extrapolation-example"]], "An optimization/minimization problem": [[23, "an-optimization-minimization-problem"]], "And what about using neural networks?": [[23, "and-what-about-using-neural-networks"]], "Another example, the moons again": [[10, "another-example-the-moons-again"]], "Applied Data Analysis and Machine Learning": [[17, null]], "Autocorrelation function": [[20, "autocorrelation-function"]], "Automatic differentiation": [[14, "automatic-differentiation"]], "Back to the Cancer Data": [[12, "back-to-the-cancer-data"]], "Bagging": [[11, "bagging"]], "Bagging Examples": [[11, "bagging-examples"]], "Basic Matrix Features": [[18, "basic-matrix-features"]], "Basic ideas of the Principal Component Analysis (PCA)": [[12, null]], "Basic math of the SVD": [[6, "basic-math-of-the-svd"]], "Basics": [[8, "basics"]], "Basics of a tree": [[10, "basics-of-a-tree"]], "Batch Normalization": [[2, "batch-normalization"]], "Bayes\u2019 Theorem and Ridge and Lasso Regression": [[6, "bayes-theorem-and-ridge-and-lasso-regression"]], "Boosting, a Bird\u2019s Eye View": [[11, "boosting-a-bird-s-eye-view"]], "Bootstrap": [[7, "bootstrap"]], "Bringing it together, first back propagation equation": [[13, "bringing-it-together-first-back-propagation-equation"]], "Building a Feed Forward Neural Network": [[2, null]], "Building a tree, regression": [[10, "building-a-tree-regression"]], "Building neural networks in Tensorflow and Keras": [[2, "building-neural-networks-in-tensorflow-and-keras"]], "CNNs in more detail, building convolutional neural networks in Tensorflow and Keras": [[4, "cnns-in-more-detail-building-convolutional-neural-networks-in-tensorflow-and-keras"]], "Cancer Data again now with Decision Trees and other Methods": [[10, "cancer-data-again-now-with-decision-trees-and-other-methods"]], "Choose cost function and optimizer": [[2, "choose-cost-function-and-optimizer"]], "Classical PCA Theorem": [[12, "classical-pca-theorem"]], "Clustering and Unsupervised Learning": [[15, null]], "Code for SVD and Inversion of Matrices": [[6, "code-for-svd-and-inversion-of-matrices"]], "Codes and Approaches": [[15, "codes-and-approaches"]], "Codes for the SVD": [[6, "codes-for-the-svd"]], "Collect and pre-process data": [[2, "collect-and-pre-process-data"]], "Communication channels": [[23, "communication-channels"]], "Compare  Bagging on Trees with Random Forests": [[11, "compare-bagging-on-trees-with-random-forests"]], "Comparing with a numerical scheme": [[3, "comparing-with-a-numerical-scheme"]], "Computing the Gini index": [[10, "computing-the-gini-index"]], "Conjugate gradient method": [[14, "conjugate-gradient-method"]], "Convex functions": [[14, "convex-functions"]], "Convolution Examples: Polynomial multiplication": [[4, "convolution-examples-polynomial-multiplication"]], "Convolution Examples: Principle of Superposition and Periodic Forces (Fourier Transforms)": [[4, "convolution-examples-principle-of-superposition-and-periodic-forces-fourier-transforms"]], "Convolutional Neural Network": [[13, "convolutional-neural-network"]], "Convolutional Neural Networks": [[4, null]], "Correlation Matrix": [[12, "correlation-matrix"]], "Course Format": [[23, "course-format"]], "Cross-validation": [[7, "cross-validation"]], "Deadlines for projects (tentative)": [[23, "deadlines-for-projects-tentative"]], "Decision trees, overarching aims": [[10, null]], "Deep learning methods": [[23, "deep-learning-methods"]], "Define model and architecture": [[2, "define-model-and-architecture"]], "Defining the cost function": [[2, "defining-the-cost-function"]], "Derivatives and the chain rule": [[13, "derivatives-and-the-chain-rule"]], "Deriving OLS from a probability distribution": [[6, "deriving-ols-from-a-probability-distribution"]], "Deriving the back propagation code for a multilayer perceptron model": [[13, "deriving-the-back-propagation-code-for-a-multilayer-perceptron-model"]], "Developing a code for doing neural networks with back propagation": [[2, "developing-a-code-for-doing-neural-networks-with-back-propagation"]], "Diagonalize the sample covariance matrix to obtain the principal components": [[12, "diagonalize-the-sample-covariance-matrix-to-obtain-the-principal-components"]], "Different kernels and Mercer\u2019s theorem": [[9, "different-kernels-and-mercer-s-theorem"]], "Disadvantages": [[10, "disadvantages"]], "Discussing the correlation data": [[0, "discussing-the-correlation-data"]], "Domains and probabilities": [[20, "domains-and-probabilities"]], "Dropout": [[2, "dropout"]], "Elements of Probability Theory and Statistical Data Analysis": [[20, null]], "Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods": [[11, null]], "Entropy and the ID3 algorithm": [[10, "entropy-and-the-id3-algorithm"]], "Essential elements of ML": [[23, "essential-elements-of-ml"]], "Evaluate model performance on test data": [[2, "evaluate-model-performance-on-test-data"]], "Example: Exponential decay": [[3, "example-exponential-decay"]], "Example: Population growth": [[3, "example-population-growth"]], "Example: The diffusion equation": [[3, "example-the-diffusion-equation"]], "Example: binary classification problem": [[2, "example-binary-classification-problem"]], "Examples": [[23, "examples"]], "Examples of likelihood functions used in logistic regression and neural networks": [[8, "examples-of-likelihood-functions-used-in-logistic-regression-and-neural-networks"]], "Examples of likelihood functions used in logistic regression and nueral networks": [[0, "examples-of-likelihood-functions-used-in-logistic-regression-and-nueral-networks"]], "Exercise 1: Setting up various Python environments": [[1, "exercise-1-setting-up-various-python-environments"], [16, "exercise-1-setting-up-various-python-environments"], [23, "exercise-1-setting-up-various-python-environments"]], "Exercise 2: making your own data and exploring scikit-learn": [[1, "exercise-2-making-your-own-data-and-exploring-scikit-learn"], [16, "exercise-2-making-your-own-data-and-exploring-scikit-learn"], [23, "exercise-2-making-your-own-data-and-exploring-scikit-learn"]], "Exercise 3: Normalizing our data": [[1, "exercise-3-normalizing-our-data"]], "Exercise 3: Split data in test and training data": [[16, "exercise-3-split-data-in-test-and-training-data"], [23, "exercise-3-split-data-in-test-and-training-data"]], "Exercise 4: Adding Ridge Regression": [[1, "exercise-4-adding-ridge-regression"]], "Exercise 5: Analytical exercises": [[1, "exercise-5-analytical-exercises"]], "Exercise:  Cross-validation as resampling techniques, adding more complexity": [[7, "exercise-cross-validation-as-resampling-techniques-adding-more-complexity"]], "Exercise: Analysis of real data": [[7, "exercise-analysis-of-real-data"]], "Exercise: Bias-variance trade-off and resampling techniques": [[7, "exercise-bias-variance-trade-off-and-resampling-techniques"]], "Exercise: Lasso Regression on the Franke function  with resampling": [[7, "exercise-lasso-regression-on-the-franke-function-with-resampling"]], "Exercise: Ordinary Least Square (OLS) on the Franke function": [[7, "exercise-ordinary-least-square-ols-on-the-franke-function"]], "Exercise: Ridge Regression on the Franke function  with resampling": [[7, "exercise-ridge-regression-on-the-franke-function-with-resampling"]], "Exercises": [[1, "exercises"], [16, "exercises"], [23, "exercises"]], "Exercises Week 42: Logistic Regression and Optimization, reminders from week 38 and week 40": [[0, null]], "Exercises and Projects": [[7, "exercises-and-projects"]], "Exercises week 34": [[16, null]], "Expectation values": [[20, "expectation-values"]], "Extending to more predictors": [[0, "extending-to-more-predictors"]], "Extremely useful tools, strongly recommended": [[23, "extremely-useful-tools-strongly-recommended"]], "Feed-forward neural networks": [[13, "feed-forward-neural-networks"]], "Feed-forward pass": [[2, "feed-forward-pass"]], "Final back propagating equation": [[13, "final-back-propagating-equation"]], "Fine-tuning neural network hyperparameters": [[2, "fine-tuning-neural-network-hyperparameters"]], "Fitting an Equation of State for Dense Nuclear Matter": [[1, "fitting-an-equation-of-state-for-dense-nuclear-matter"], [23, "fitting-an-equation-of-state-for-dense-nuclear-matter"]], "From one to many layers, the universal approximation theorem": [[13, "from-one-to-many-layers-the-universal-approximation-theorem"]], "Further Dimensionality Remarks": [[4, "further-dimensionality-remarks"]], "Further properties (important for our analyses later)": [[6, "further-properties-important-for-our-analyses-later"]], "Gaussian Elimination": [[18, "gaussian-elimination"]], "General Features": [[10, "general-features"]], "General linear models and linear algebra": [[23, "general-linear-models-and-linear-algebra"]], "Generalizing the fitting procedure as a linear algebra problem": [[23, "generalizing-the-fitting-procedure-as-a-linear-algebra-problem"], [23, "id1"]], "Generative Adversarial Networks": [[5, "generative-adversarial-networks"]], "Generative Models": [[5, "generative-models"]], "Geometric Interpretation and link with Singular Value Decomposition": [[12, "geometric-interpretation-and-link-with-singular-value-decomposition"]], "Gradient Boosting, Classification Example": [[11, "gradient-boosting-classification-example"]], "Gradient Boosting, Examples of Regression": [[11, "gradient-boosting-examples-of-regression"]], "Gradient Clipping": [[2, "gradient-clipping"]], "Gradient boosting: Basics with Steepest Descent/Functional Gradient Descent": [[11, "gradient-boosting-basics-with-steepest-descent-functional-gradient-descent"]], "Gradient descent": [[3, "gradient-descent"]], "Gradient descent and Logistic regression": [[0, "gradient-descent-and-logistic-regression"]], "Grading": [[21, "grading"], [23, "grading"]], "Housing data, the code": [[1, "housing-data-the-code"]], "Hyperplanes and all that": [[9, "hyperplanes-and-all-that"]], "Important Matrix and vector handling packages": [[18, "important-matrix-and-vector-handling-packages"]], "Improving performance": [[2, "improving-performance"]], "In summary": [[21, "in-summary"]], "Including Stochastic Gradient Descent with Autograd": [[14, "including-stochastic-gradient-descent-with-autograd"]], "Including more classes": [[0, "including-more-classes"]], "Incremental PCA": [[12, "incremental-pca"]], "Installing R, C++, cython or Julia": [[23, "installing-r-c-cython-or-julia"]], "Installing R, C++, cython, Numba etc": [[23, "installing-r-c-cython-numba-etc"]], "Instructor information": [[21, "instructor-information"]], "Interpretations and optimizing our parameters": [[23, "interpretations-and-optimizing-our-parameters"], [23, "id2"], [23, "id3"]], "Introducing JAX": [[14, "introducing-jax"]], "Introducing the Covariance and Correlation functions": [[12, "introducing-the-covariance-and-correlation-functions"]], "Introduction": [[1, "introduction"], [7, "introduction"], [17, "introduction"], [18, "introduction"]], "Iterative Fitting, Classification and AdaBoost": [[11, "iterative-fitting-classification-and-adaboost"]], "Iterative Fitting, Regression and Squared-error Cost Function": [[11, "iterative-fitting-regression-and-squared-error-cost-function"]], "Kernel PCA": [[12, "kernel-pca"]], "Kernels and non-linearity": [[9, "kernels-and-non-linearity"]], "LU Decomposition, the inverse of a matrix": [[18, "lu-decomposition-the-inverse-of-a-matrix"]], "Layers": [[2, "layers"]], "Layers used to build CNNs": [[4, "layers-used-to-build-cnns"]], "Learning outcomes": [[17, "learning-outcomes"], [23, "learning-outcomes"]], "Lectures and ComputerLab": [[23, "lectures-and-computerlab"]], "Limitations of supervised learning with deep networks": [[2, "limitations-of-supervised-learning-with-deep-networks"]], "Linear Algebra, Handling of Arrays and more Python Features": [[18, null]], "Linear Regression": [[1, null]], "Linear Regression, basic elements": [[1, "linear-regression-basic-elements"]], "Linking Bayes\u2019 Theorem with Ridge and Lasso Regression": [[6, "linking-bayes-theorem-with-ridge-and-lasso-regression"]], "Linking the regression analysis with a statistical interpretation": [[6, "linking-the-regression-analysis-with-a-statistical-interpretation"]], "Linking with the SVD": [[6, "linking-with-the-svd"]], "Links to relevant courses at the University of Oslo": [[22, "links-to-relevant-courses-at-the-university-of-oslo"]], "Logistic Regression": [[8, null], [8, "id1"]], "MNIST and GANs": [[5, "mnist-and-gans"]], "Machine Learning": [[23, "machine-learning"]], "Machine learning": [[17, "machine-learning"]], "Main textbooks": [[23, "main-textbooks"]], "Making a tree": [[10, "making-a-tree"]], "Making your own Bootstrap: Changing the Level of the Decision Tree": [[11, "making-your-own-bootstrap-changing-the-level-of-the-decision-tree"]], "Mathematical Interpretation of Ordinary Least Squares": [[6, "mathematical-interpretation-of-ordinary-least-squares"]], "Mathematical optimization of convex functions": [[9, "mathematical-optimization-of-convex-functions"]], "Mathematics of CNNs": [[4, "mathematics-of-cnns"]], "Mathematics of the SVD and implications": [[6, "mathematics-of-the-svd-and-implications"]], "Matrices in Python": [[23, "matrices-in-python"]], "Matrix  multiplication": [[2, "matrix-multiplication"]], "Matrix-vector notation  and activation": [[13, "matrix-vector-notation-and-activation"]], "Meet the  covariance!": [[20, "meet-the-covariance"]], "Meet the Covariance Matrix": [[6, "meet-the-covariance-matrix"]], "Meet the Pandas": [[23, "meet-the-pandas"]], "Minimizing the cross entropy": [[0, "minimizing-the-cross-entropy"]], "Momentum based GD": [[14, "momentum-based-gd"]], "More classes": [[0, "more-classes"]], "More complicated Example: The Ising model": [[7, "more-complicated-example-the-ising-model"]], "More on Dimensionalities": [[4, "more-on-dimensionalities"]], "More on Rescaling data": [[7, "more-on-rescaling-data"]], "Multilayer perceptrons": [[13, "multilayer-perceptrons"]], "Network requirements": [[3, "network-requirements"]], "Neural Networks vs CNNs": [[4, "neural-networks-vs-cnns"]], "Neural networks": [[13, null]], "Numerical experiments and the covariance, central limit theorem": [[20, "numerical-experiments-and-the-covariance-central-limit-theorem"]], "Numpy and arrays": [[18, "numpy-and-arrays"], [23, "numpy-and-arrays"]], "Numpy examples and Important Matrix and vector handling packages": [[23, "numpy-examples-and-important-matrix-and-vector-handling-packages"]], "Optimization, the central part of any Machine Learning algortithm": [[14, null]], "Optimizing our parameters": [[23, "optimizing-our-parameters"]], "Optimizing our parameters, more details": [[23, "optimizing-our-parameters-more-details"]], "Optimizing the cost function": [[2, "optimizing-the-cost-function"]], "Organizing our data": [[1, "organizing-our-data"], [23, "organizing-our-data"]], "Other Matrix and Vector Operations": [[18, "other-matrix-and-vector-operations"]], "Other Types of Recurrent Neural Networks": [[5, "other-types-of-recurrent-neural-networks"]], "Other courses on Data science and Machine Learning  at UiO": [[23, "other-courses-on-data-science-and-machine-learning-at-uio"]], "Other courses on Data science and Machine Learning  at UiO, contn": [[23, "other-courses-on-data-science-and-machine-learning-at-uio-contn"]], "Other measures in classification studies: Cancer Data  again": [[0, "other-measures-in-classification-studies-cancer-data-again"]], "Other popular texts": [[23, "other-popular-texts"]], "Other techniques": [[12, "other-techniques"]], "Other types of networks": [[13, "other-types-of-networks"]], "Other ways of visualizing the trees": [[10, "other-ways-of-visualizing-the-trees"]], "Our model for the nuclear binding energies": [[23, "our-model-for-the-nuclear-binding-energies"]], "Overview of first week": [[23, "overview-of-first-week"]], "Own code for Ordinary Least Squares": [[23, "own-code-for-ordinary-least-squares"]], "PCA and scikit-learn": [[12, "pca-and-scikit-learn"]], "Pandas AI": [[23, "pandas-ai"]], "Partial Differential Equations": [[3, "partial-differential-equations"]], "Practical tips": [[14, "practical-tips"]], "Practicalities": [[21, "practicalities"]], "Predicting New Points With A Trained Recurrent Neural Network": [[5, "predicting-new-points-with-a-trained-recurrent-neural-network"]], "Prerequisites": [[23, "prerequisites"]], "Prerequisites and background": [[17, "prerequisites-and-background"]], "Prerequisites: Collect and pre-process data": [[4, "prerequisites-collect-and-pre-process-data"]], "Probability Distribution Functions": [[20, "probability-distribution-functions"]], "Program for stochastic gradient": [[14, "program-for-stochastic-gradient"]], "Properties of PDFs": [[20, "properties-of-pdfs"]], "Pros and cons of trees, pros": [[10, "pros-and-cons-of-trees-pros"]], "Python installers": [[17, "python-installers"], [23, "python-installers"]], "RMS prop": [[14, "rms-prop"]], "Random Numbers": [[20, "random-numbers"]], "Random forests": [[11, "random-forests"]], "Randomized PCA": [[12, "randomized-pca"]], "Reading material": [[23, "reading-material"]], "Reading suggestions week 34": [[23, "reading-suggestions-week-34"]], "Recurrent neural networks": [[13, "recurrent-neural-networks"]], "Recurrent neural networks: Overarching view": [[5, null]], "Reducing the number of degrees of freedom, overarching view": [[1, "reducing-the-number-of-degrees-of-freedom-overarching-view"]], "Reformulating the problem": [[3, "reformulating-the-problem"]], "Regression Case": [[11, "regression-case"]], "Regression analysis, overarching aims": [[23, "regression-analysis-overarching-aims"]], "Regression analysis, overarching aims II": [[23, "regression-analysis-overarching-aims-ii"]], "Regularization": [[2, "regularization"]], "Reminder on Statistics": [[7, "reminder-on-statistics"]], "Replace or not": [[14, "replace-or-not"]], "Required Technologies": [[17, "required-technologies"]], "Resampling Methods": [[7, null]], "Resampling methods": [[7, "id1"]], "Resources on differential equations and deep learning": [[3, "resources-on-differential-equations-and-deep-learning"]], "Revisiting our Linear Regression Solvers": [[14, "revisiting-our-linear-regression-solvers"]], "Rewriting the fitting procedure as a linear algebra problem": [[23, "rewriting-the-fitting-procedure-as-a-linear-algebra-problem"]], "Rewriting the fitting procedure as a linear algebra problem, more details": [[23, "rewriting-the-fitting-procedure-as-a-linear-algebra-problem-more-details"]], "Ridge and Lasso Regression": [[6, null], [6, "id1"]], "Same code but now with momentum gradient descent": [[14, "same-code-but-now-with-momentum-gradient-descent"]], "Schedule first week": [[23, "schedule-first-week"]], "Schematic Regression Procedure": [[10, "schematic-regression-procedure"]], "Setting up the Back propagation algorithm": [[13, "setting-up-the-back-propagation-algorithm"]], "Setting up the network using Autograd; The full program": [[3, "setting-up-the-network-using-autograd-the-full-program"]], "Similar (second order function now) problem but now with AdaGrad": [[14, "similar-second-order-function-now-problem-but-now-with-adagrad"]], "Simple Python Code to read in Data and perform Classification": [[10, "simple-python-code-to-read-in-data-and-perform-classification"]], "Simple linear regression model using scikit-learn": [[1, "simple-linear-regression-model-using-scikit-learn"], [23, "simple-linear-regression-model-using-scikit-learn"]], "Software and needed installations": [[23, "software-and-needed-installations"]], "Solving Differential Equations  with Deep Learning": [[3, null]], "Solving the one dimensional Poisson equation": [[3, "solving-the-one-dimensional-poisson-equation"]], "Solving the wave equation with Neural Networks": [[3, "solving-the-wave-equation-with-neural-networks"]], "Some famous Matrices": [[18, "some-famous-matrices"]], "Some simple problems": [[14, "some-simple-problems"]], "Splitting our Data in Training and Test data": [[1, "splitting-our-data-in-training-and-test-data"], [23, "splitting-our-data-in-training-and-test-data"]], "Standard steepest descent": [[14, "standard-steepest-descent"]], "Statistical analysis and optimization of data": [[17, "statistical-analysis-and-optimization-of-data"], [23, "statistical-analysis-and-optimization-of-data"]], "Steepest descent": [[14, "steepest-descent"]], "Stochastic Gradient Descent (SGD)": [[14, "stochastic-gradient-descent-sgd"]], "Stochastic variables and the main concepts, the discrete case": [[20, "stochastic-variables-and-the-main-concepts-the-discrete-case"]], "Support Vector Machines, overarching aims": [[9, null]], "Systematic reduction": [[4, "systematic-reduction"]], "Teachers": [[23, "teachers"]], "Teachers and Grading": [[21, null]], "Teaching Assistants Fall semester 2023": [[21, "teaching-assistants-fall-semester-2023"]], "Teaching schedule with links to material": [[19, null]], "Testing the Means Squared Error as function of Complexity": [[1, "testing-the-means-squared-error-as-function-of-complexity"]], "Textbooks": [[22, null]], "The Algorithm before theorem": [[12, "the-algorithm-before-theorem"]], "The Boston housing data example": [[1, "the-boston-housing-data-example"]], "The Breast Cancer Data, now with Keras": [[2, "the-breast-cancer-data-now-with-keras"]], "The CART algorithm for Classification": [[10, "the-cart-algorithm-for-classification"]], "The CART algorithm for Regression": [[10, "the-cart-algorithm-for-regression"]], "The CIFAR01 data set": [[4, "the-cifar01-data-set"]], "The MNIST dataset again": [[4, "the-mnist-dataset-again"]], "The RELU function family": [[2, "the-relu-function-family"]], "The Softmax function": [[2, "the-softmax-function"]], "The \\chi^2 function": [[1, "the-chi-2-function"], [23, "the-chi-2-function"], [23, "id4"], [23, "id5"], [23, "id6"], [23, "id7"], [23, "id8"]], "The bias-variance tradeoff": [[7, "the-bias-variance-tradeoff"]], "The code": [[23, "the-code"]], "The code for solving the ODE": [[3, "the-code-for-solving-the-ode"]], "The cost function": [[0, "the-cost-function"]], "The course has two central parts": [[17, "the-course-has-two-central-parts"]], "The logistic function": [[0, "the-logistic-function"], [8, "the-logistic-function"]], "The moons example": [[9, "the-moons-example"]], "The multilayer  perceptron (MLP)": [[13, "the-multilayer-perceptron-mlp"]], "The network with one input layer, specified number of hidden layers, and one output layer": [[3, "the-network-with-one-input-layer-specified-number-of-hidden-layers-and-one-output-layer"]], "The singular value decomposition": [[6, "the-singular-value-decomposition"]], "The two-dimensional case": [[9, "the-two-dimensional-case"]], "To our real data: nuclear binding energies. Brief reminder on masses and binding energies": [[23, "to-our-real-data-nuclear-binding-energies-brief-reminder-on-masses-and-binding-energies"]], "Topics covered in this course: Statistical analysis and optimization of data": [[23, "topics-covered-in-this-course-statistical-analysis-and-optimization-of-data"]], "Towards the PCA theorem": [[12, "towards-the-pca-theorem"]], "Train and test datasets": [[2, "train-and-test-datasets"]], "Two parameters": [[0, "two-parameters"]], "Two-dimensional Objects": [[4, "two-dimensional-objects"]], "Type of problem": [[3, "type-of-problem"]], "Types of Machine Learning": [[23, "types-of-machine-learning"]], "Useful Python libraries": [[17, "useful-python-libraries"], [23, "useful-python-libraries"]], "Using Autograd": [[14, "using-autograd"]], "Using forward Euler to solve the ODE": [[3, "using-forward-euler-to-solve-the-ode"]], "Using gradient descent methods, limitations": [[14, "using-gradient-descent-methods-limitations"]], "Using the correlation matrix": [[0, "using-the-correlation-matrix"]], "Visualization": [[2, "visualization"], [2, "id1"]], "Visualizing the Tree, Classification": [[10, "visualizing-the-tree-classification"]], "Week 34: Introduction to the course, Logistics and Practicalities": [[23, null]], "Weekly Schedule": [[19, "weekly-schedule"]], "What is Machine Learning?": [[1, "what-is-machine-learning"]], "What is a good model?": [[1, "what-is-a-good-model"], [23, "what-is-a-good-model"]], "What is a good model? 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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", "Applied Data Analysis and Machine Learning", "<span class=\"section-number\">2. </span>Linear Algebra, Handling of Arrays and more Python Features", "Teaching schedule with links to material", "<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"], "titleterms": {"": [9, 11], "1": [1, 16, 23], "2": [1, 16, 23], "2023": 21, "3": [1, 16, 23], "34": [16, 23], "38": 0, "4": 1, "40": 0, "42": 0, "5": 1, "A": [0, 1, 2, 5, 9, 10, 23], "And": 23, "In": 21, "Ising": 7, "The": [0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 12, 13, 17, 23], "To": 23, "With": 5, "about": 23, "activ": [2, 13], "ad": [1, 7, 23], "adaboost": 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