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Github Uu Sml Course Sml Public Course Material For 1rt700

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github uu sml course sml public course material for 1rt700

This repository is used to host the files needed for the exercise sessions and the computer lab in the course Statistical Machine Learning at Uppsala University. The material associated with each session is given below together with a set of recommended problems. For each session, the material consists of the following: Data used in the computer classes can be downloaded directly in the notebooks. For offline use, we recommend you download the whole repository and make the necessary changes to the notebook by commenting/uncommenting appropriate lines. For the computer lab about deep learning the following resources are available:

This repository is used to host the files needed for the exercise sessions and the computer lab in the course Statistical Machine Learning at Uppsala University. The material associated with each session is given below together with a set of recommended problems. For each session, the material consists of the following: Data used in the computer classes can be downloaded directly in the notebooks. For offline use, we recommend you download the whole repository and make the necessary changes to the notebook by commenting/uncommenting appropriate lines. For the computer lab about deep learning the following resources are available:

There was an error while loading. Please reload this page. Implement the linear regression problems from Exercises 1.1(a), (b), (c), (d) and (e) in Python using matrix multiplications. A matrix $$ \textbf{X} = \begin{bmatrix} 1 & 2 \\ 1 & 3 \\ \end{bmatrix} $$ can be constructed with numpy as X=np.array([[1, 2], [1, 3]]) (Make sure that numpy has been imported. Here it is imported as np). The commands for matrix multiplication and transpose in numpy are @ or np.matmul and .T or np.transpose() respectively.

A system of linear equations $\textbf{A}x=\textbf{b}$ can be solved using np.linalg.solve(A,b). A $k \times k$ unit matrix can be constructed with np.eye(k). Assume that you record a scalar input $x$ and a scalar output $y$. First, you record $x_1 = 2, y_1 = -1$, and thereafter $x_2 = 3, y_2 = 1$. Assume a linear regression model $y = \theta_0 + \theta_1 x + \epsilon$ and learn the parameters with maximum likelihood $\widehat{\boldsymbol{\theta}}$ with the assumption $\epsilon \sim \mathcal{N}(0,\sigma_\epsilon^2)$. Use the model to predict the output for the test input $x_\star = 4$, and plot the data and the model.

Now, assume you have made a third observation $y_3 = 2$ for $x_3 = 4$ (is that what you predicted in (a)?). Update the parameters $\widehat{\boldsymbol{\theta}}$ to all 3 data samples, add the new model to the plot (together with the new data point) and find the prediction for $x_\star = 5$. Repeat (b), but this time using a model without intercept term, i.e., $y = \theta_1x + \epsilon$. Repeat (b), but this time using Ridge Regression with $\gamma=1$ instead. This repository is used to host the files needed for the exercise sessions and the computer lab in the course Advanced Probabilistic Machine Learning at Uppsala University. The material associated with each session listed below is given together with a set of recommended problems.

For each session, the material consists of the following: Data used in the computer classes can be downloaded directly in the notebooks. For offline use, we recommend you download the whole repository and make the necessary changes to the notebook by commenting/uncommenting appropriate lines. For the computer lab about unsupervised learning the following resources are available: In this exercise we will return to the biopsy data set also used in Exercise 4.1 (Lesson 4). We will try to determine suitable value of $k$ in $k$-NN for this data.

For simplicity, we will only consider the three attributes in columns V3, V4and V5 in this problem. Consider all data as training data. Investigate how the training error varies with different values of $k$ (hint: use a for-loop). Which $k$ gives the best result? Is it a good choice of $k$? Split the data randomly into a training and validation set, and see how well you perform on the validation set.

(Previously, we have used the terminology "training" and "test" set. If the other set (not the training set) is used to make design decisions, such as choosing $k$, it is really not a test set, but rather a "validation" set. Hence the terminology.) Which $k$ gives the best result? Perform (b) 10 times for different validation sets and average the result. Which $k$ gives the best result? Perform 10-fold cross-validation by first randomly permute the data set, divide the data set into 10 equally sized parts and loop through them by taking one part as validation set and the rest as...

Which $k$ gives the best result? Course material for 1RT700 Statistical Machine Learning Python package for evaluating model calibration in classification Code for https://www.youtube.com/watch?v=aUkBa1zMKv4 Material for the seminar course: "The unreasonable effectiveness of overparameterized machine learning models" held at Uppsala University. Course material for 1RT705/1RT003 Advanced Probabilistic Machine Learning

There was an error while loading. Please reload this page. You can create a release to package software, along with release notes and links to binary files, for other people to use. Learn more about releases in our docs. In this exercise session, we will review the fundamentals of three important Python libraries: Numpy, Pandas and Matplotlib. Throughout the course, you will need to get familiar with several Python libraries that provide convenient functionality for machine learning purposes.

It is good to get into the habit of using the available documentation to your advantage. Some efficient ways of doing this are described below: In Python, the help()-function can be used to display the documentation for a module, function, or object. When called with no arguments it opens an interactive help session. When called with a specific object as an argument, it displays the documentation for that object. For example, you can use help(print) to view the documentation for the built-in print function, or help(str) to view the documentation for the str class.

Additionally, you can use the dir()-function to get all methods and properties of the object passed as an argument to it. It can be used to check all the attributes of a module or class. For example, dir(str) will give the methods and properties of the str class. In Jupyter notebook, shift+tab is a keyboard shortcut that can be used to access the documentation for the function or object that appears immediately before the cursor. When you press shift+tab, a small pop-up window will appear that contains information about the function or object, including its arguments and their types. Pressing shift+tab multiple times will cycle through different levels of documentation.

If nothing is selected it will show the tip of the current cell. When running notebooks on Google Colab, you can trigger the documentation by clicking the function and then hovering over it with the cursor. Before getting started, we make sure that the libraries are properly imported in our current environment. Do this by running the cell below. Vectors, matrices and tensors can be represented as numpy arrays. Numpy arrays are often initialized from regular Python lists.

For example, the Python list [1, 2, 3] can be converted into a 1D numpy array using the command np.array([1, 2, 3]). Create this numpy array in the cell below and print its shape. You can find the shape of a numpy array A using np.shape(A).

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This repository is used to host the files needed for the exercise sessions and the computer lab in the course Statistical Machine Learning at Uppsala University. The material associated with each session is given below together with a set of recommended problems. For each session, the material consists of the following: Data used in the computer classes can be downloaded directly in the notebooks....

This Repository Is Used To Host The Files Needed For

This repository is used to host the files needed for the exercise sessions and the computer lab in the course Statistical Machine Learning at Uppsala University. The material associated with each session is given below together with a set of recommended problems. For each session, the material consists of the following: Data used in the computer classes can be downloaded directly in the notebooks....

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There was an error while loading. Please reload this page. Implement the linear regression problems from Exercises 1.1(a), (b), (c), (d) and (e) in Python using matrix multiplications. A matrix $$ \textbf{X} = \begin{bmatrix} 1 & 2 \\ 1 & 3 \\ \end{bmatrix} $$ can be constructed with numpy as X=np.array([[1, 2], [1, 3]]) (Make sure that numpy has been imported. Here it is imported as np). The comm...

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