From 133fe9d0eacb83f1ec187ba5091b696b972f8dd6 Mon Sep 17 00:00:00 2001 From: mhjensen Date: Thu, 25 Oct 2018 04:41:10 +0200 Subject: [PATCH] update on dim red --- doc/pub/DimRed/html/._DimRed-bs000.html | 42 +++++- doc/pub/DimRed/html/._DimRed-bs001.html | 44 +++++- doc/pub/DimRed/html/._DimRed-bs002.html | 176 +++++++--------------- doc/pub/DimRed/html/._DimRed-bs003.html | 86 +++++++---- doc/pub/DimRed/html/._DimRed-bs004.html | 71 +++++++-- doc/pub/DimRed/html/._DimRed-bs005.html | 64 +++++--- doc/pub/Regression/ipynb/Regression.ipynb | 44 +++--- doc/src/DimRed/DimRed.do.txt | 143 ++++++------------ doc/web/course.do.txt | 20 +-- doc/web/course.html | 32 ++-- 10 files changed, 373 insertions(+), 349 deletions(-) diff --git a/doc/pub/DimRed/html/._DimRed-bs000.html b/doc/pub/DimRed/html/._DimRed-bs000.html index 61c7598da..464c7364f 100644 --- a/doc/pub/DimRed/html/._DimRed-bs000.html +++ b/doc/pub/DimRed/html/._DimRed-bs000.html @@ -45,13 +45,33 @@ Automatically generated HTML file from DocOnce source None, '___sec0'), ('Principal Component Analysis', 2, None, '___sec1'), - ('Kernel PCA', 2, None, '___sec2'), - ('LLE', 2, None, '___sec3'), - ('Other techniques', 2, None, '___sec4')]} + ('PCA and scikit-learn', 2, None, '___sec2'), + ('More on the PCA', 2, None, '___sec3'), + ('Incremental PCA', 2, None, '___sec4'), + ('Randomized PCA', 2, None, '___sec5'), + ('Kernel PCA', 2, None, '___sec6'), + ('LLE', 2, None, '___sec7'), + ('Other techniques', 2, None, '___sec8')]} end of tocinfo --> + + + + + + +
@@ -71,9 +91,13 @@ end of tocinfo --> @@ -108,7 +132,7 @@ end of tocinfo -->
[2] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University

-

Oct 24, 2018

+

Oct 25, 2018


@@ -127,6 +151,10 @@ end of tocinfo -->

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    • diff --git a/doc/pub/DimRed/html/._DimRed-bs001.html b/doc/pub/DimRed/html/._DimRed-bs001.html index 05a85c0cc..ad53ab15e 100644 --- a/doc/pub/DimRed/html/._DimRed-bs001.html +++ b/doc/pub/DimRed/html/._DimRed-bs001.html @@ -45,13 +45,33 @@ Automatically generated HTML file from DocOnce source None, '___sec0'), ('Principal Component Analysis', 2, None, '___sec1'), - ('Kernel PCA', 2, None, '___sec2'), - ('LLE', 2, None, '___sec3'), - ('Other techniques', 2, None, '___sec4')]} + ('PCA and scikit-learn', 2, None, '___sec2'), + ('More on the PCA', 2, None, '___sec3'), + ('Incremental PCA', 2, None, '___sec4'), + ('Randomized PCA', 2, None, '___sec5'), + ('Kernel PCA', 2, None, '___sec6'), + ('LLE', 2, None, '___sec7'), + ('Other techniques', 2, None, '___sec8')]} end of tocinfo --> + + + + + + +
    @@ -71,9 +91,13 @@ end of tocinfo --> @@ -102,8 +126,8 @@ Fortunately, in real-world problems, it is often possible to reduce the number o turning an intractable problem into a tractable one.

    -and we will go through three of the most popular dimensionality -reduction techniques: PCA, Kernel PCA, and LLE. +Here we will discuss some of the most popular dimensionality +reduction techniques: the principal component analysis PCA, Kernel PCA, and Locally Linear Embedding (LLE).

    @@ -121,6 +145,10 @@ reduction techniques: PCA, Kernel PCA, and LLE.
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    • diff --git a/doc/pub/DimRed/html/._DimRed-bs002.html b/doc/pub/DimRed/html/._DimRed-bs002.html index 7b9bf9b76..dab0b773d 100644 --- a/doc/pub/DimRed/html/._DimRed-bs002.html +++ b/doc/pub/DimRed/html/._DimRed-bs002.html @@ -45,13 +45,33 @@ Automatically generated HTML file from DocOnce source None, '___sec0'), ('Principal Component Analysis', 2, None, '___sec1'), - ('Kernel PCA', 2, None, '___sec2'), - ('LLE', 2, None, '___sec3'), - ('Other techniques', 2, None, '___sec4')]} + ('PCA and scikit-learn', 2, None, '___sec2'), + ('More on the PCA', 2, None, '___sec3'), + ('Incremental PCA', 2, None, '___sec4'), + ('Randomized PCA', 2, None, '___sec5'), + ('Kernel PCA', 2, None, '___sec6'), + ('LLE', 2, None, '___sec7'), + ('Other techniques', 2, None, '___sec8')]} end of tocinfo --> + + + + + + +
    @@ -71,9 +91,13 @@ end of tocinfo --> @@ -97,133 +121,31 @@ Principal Component Analysis (PCA) is by far the most popular dimensionality red First it identifies the hyperplane that lies closest to the data, and then it projects the data onto it.

    -The following Python code uses NumPy’s svd() function to obtain all the principal components of the -training set, then extracts the first two PCs: -X_centered = X - X.mean(axis=0) -U, s, V = np.linalg.svd(X_centered) -c1 = V.T[:, 0] -c2 = V.T[:, 1] - +The following Python code uses NumPy’s svd() function to obtain all the principal components of the +training set, then extracts the first two principal components

    -PCA assumes that the dataset is centered around the origin. As we will see, Scikit-Learn’s PCA classes take care of centering + + +

    X_centered = X - X.mean(axis=0)
+U, s, V = np.linalg.svd(X_centered)
+c1 = V.T[:, 0]
+c2 = V.T[:, 1]
+
    +

    +PCA assumes that the dataset is centered around the origin. Scikit-Learn’s PCA classes take care of centering the data for you. However, if you implement PCA yourself (as in the preceding example), or if you use other libraries, don’t forget to center the data first.

    Once you have identified all the principal components, you can reduce the dimensionality of the dataset -down to d dimensions by projecting it onto the hyperplane defined by the first d principal components. -Selecting this hyperplane ensures that the projection will preserve as much variance as possible. For -example, in Figure 8-2 the 3D dataset is projected down to the 2D plane defined by the first two principal -components, preserving a large part of the dataset’s variance. As a result, the 2D projection looks very -much like the original 3D dataset. - +down to \( d \) dimensions by projecting it onto the hyperplane defined by the first \( d \) principal components. +Selecting this hyperplane ensures that the projection will preserve as much variance as possible.

    -W2 = V.T[:, :2] -X2D = X_centered.dot(W2) - -

    -Scikit-Learn’s PCA class implements PCA using SVD decomposition just like we did before. The -following code applies PCA to reduce the dimensionality of the dataset down to two dimensions (note -that it automatically takes care of centering the data): -from sklearn.decomposition import PCA -pca = PCA(n_components = 2) -X2D = pca.fit_transform(X) -After fitting the PCA transformer to the dataset, you can access the principal components using the -components_ variable (note that it contains the PCs as horizontal vectors, so, for example, the first -principal component is equal to pca.components_.T[:, 0]). - -

    -Another very useful piece of information is the explained variance ratio of each principal component, -available via the explained_variance_ratio_ variable. It indicates the proportion of the dataset’s -variance that lies along the axis of each principal component. For example, let’s look at the explained -variance ratios of the first two components of the 3D dataset represented in Figure 8-2: ->>> print(pca.explained_variance_ratio_) -array([ 0.84248607, 0.14631839]) -This tells you that 84.2% of the dataset’s variance lies along the first axis, and 14.6% lies along the -second axis. This leaves less than 1.2% for the third axis, so it is reasonable to assume that it probably -carries little information. - -

    -Instead of arbitrarily choosing the number of dimensions to reduce down to, it is generally preferable to -choose the number of dimensions that add up to a sufficiently large portion of the variance (e.g., 95%). -Unless, of course, you are reducing dimensionality for data visualization — in that case you will -generally want to reduce the dimensionality down to 2 or 3. -The following code computes PCA without reducing dimensionality, then computes the minimum number -of dimensions required to preserve 95% of the training set’s variance: -pca = PCA() -pca.fit(X) -cumsum = np.cumsum(pca.explained_variance_ratio_) -d = np.argmax(cumsum >= 0.95) + 1 -You could then set n_components=d and run PCA again. However, there is a much better option: instead -of specifying the number of principal components you want to preserve, you can set n_components to be -a float between 0.0 and 1.0, indicating the ratio of variance you wish to preserve: -pca = PCA(n_components=0.95) -X_reduced = pca.fit_transform(X) - -

    -Obviously after dimensionality reduction, the training set takes up much less space. For example, try -applying PCA to the MNIST dataset while preserving 95% of its variance. You should find that each -instance will have just over 150 features, instead of the original 784 features. So while most of the -variance is preserved, the dataset is now less than 20% of its original size! This is a reasonable -compression ratio, and you can see how this can speed up a classification algorithm (such as an SVM -classifier) tremendously. -It is also possible to decompress the reduced dataset back to 784 dimensions by applying the inverse -transformation of the PCA projection. Of course this won’t give you back the original data, since the -projection lost a bit of information (within the 5% variance that was dropped), but it will likely be quite -close to the original data. The mean squared distance between the original data and the reconstructed data -(compressed and then decompressed) is called the reconstruction error. For example, the following code -compresses the MNIST dataset down to 154 dimensions, then uses the inverse_transform() method to -decompress it back to 784 dimensions. Figure 8-9 shows a few digits from the original training set (on the -left), and the corresponding digits after compression and decompression. You can see that there is a slight -image quality loss, but the digits are still mostly intact. -pca = PCA(n_components = 154) -X_mnist_reduced = pca.fit_transform(X_mnist) -X_mnist_recovered = pca.inverse_transform(X_mnist_reduced) -Figure - -

    -Incremental PCA -One problem with the preceding implementation of PCA is that it requires the whole training set to fit in -memory in order for the SVD algorithm to run. Fortunately, Incremental PCA (IPCA) algorithms have -been developed: you can split the training set into mini-batches and feed an IPCA algorithm one minibatch -at a time. This is useful for large training sets, and also to apply PCA online (i.e., on the fly, as new -instances arrive). -The following code splits the MNIST dataset into 100 mini-batches (using NumPy’s array_split() -function) and feeds them to Scikit-Learn’s IncrementalPCA class5 to reduce the dimensionality of the -MNIST dataset down to 154 dimensions (just like before). Note that you must call the partial_fit() -method with each mini-batch rather than the fit() method with the whole training set: -from sklearn.decomposition import IncrementalPCA -n_batches = 100 -inc_pca = IncrementalPCA(n_components=154) -for X_batch in np.array_split(X_mnist, n_batches): -inc_pca.partial_fit(X_batch) -X_mnist_reduced = inc_pca.transform(X_mnist) - -

    -Alternatively, you can use NumPy’s memmap class, which allows you to manipulate a large array stored in -a binary file on disk as if it were entirely in memory; the class loads only the data it needs in memory, -when it needs it. Since the IncrementalPCA class uses only a small part of the array at any given time, -the memory usage remains under control. This makes it possible to call the usual fit() method, as you -can see in the following code: -X_mm = np.memmap(filename, dtype="float32", mode="readonly", shape=(m, n)) -batch_size = m // n_batches -inc_pca = IncrementalPCA(n_components=154, batch_size=batch_size) -inc_pca.fit(X_mm) - -

    -Randomized PCA -Scikit-Learn offers yet another option to perform PCA, called Randomized PCA. This is a stochastic -algorithm that quickly finds an approximation of the first d principal components. Its computational -complexity is O(m × d2) + O(d3), instead of O(m × n2) + O(n3), so it is dramatically faster than the -previous algorithms when d is much smaller than n. -rnd_pca = PCA(n_components=154, svd_solver="randomized") -X_reduced = rnd_pca.fit_transform(X_mnist) - -

    -

    -
    - + +
    W2 = V.T[:, :2]
+X2D = X_centered.dot(W2)
+

    @@ -235,6 +157,10 @@ X_reduced = rnd_pca.fit_transform(X_mnist)

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    • diff --git a/doc/pub/DimRed/html/._DimRed-bs003.html b/doc/pub/DimRed/html/._DimRed-bs003.html index 465d81525..361939a72 100644 --- a/doc/pub/DimRed/html/._DimRed-bs003.html +++ b/doc/pub/DimRed/html/._DimRed-bs003.html @@ -45,13 +45,33 @@ Automatically generated HTML file from DocOnce source None, '___sec0'), ('Principal Component Analysis', 2, None, '___sec1'), - ('Kernel PCA', 2, None, '___sec2'), - ('LLE', 2, None, '___sec3'), - ('Other techniques', 2, None, '___sec4')]} + ('PCA and scikit-learn', 2, None, '___sec2'), + ('More on the PCA', 2, None, '___sec3'), + ('Incremental PCA', 2, None, '___sec4'), + ('Randomized PCA', 2, None, '___sec5'), + ('Kernel PCA', 2, None, '___sec6'), + ('LLE', 2, None, '___sec7'), + ('Other techniques', 2, None, '___sec8')]} end of tocinfo --> + + + + + + +
    @@ -71,9 +91,13 @@ end of tocinfo --> @@ -87,33 +111,35 @@ end of tocinfo -->

     

     

     

    - + -

    Kernel PCA

    -
    -
    -

    +

    PCA and scikit-learn

    -Kernel PCA -The kernel trick is a mathematical technique that implicitly maps instances into a -very high-dimensional space (called the feature space), enabling nonlinear classification and regression -with Support Vector Machines. Recall that a linear decision boundary in the high-dimensional feature -space corresponds to a complex nonlinear decision boundary in the original space. -It turns out that the same trick can be applied to PCA, making it possible to perform complex nonlinear -projections for dimensionality reduction. This is called Kernel PCA (kPCA). It is often good at -preserving clusters of instances after projection, or sometimes even unrolling datasets that lie close to a -twisted manifold. -For example, the following code uses Scikit-Learn’s KernelPCA class to perform kPCA with an -from sklearn.decomposition import KernelPCA -rbf_pca = KernelPCA(n_components = 2, kernel="rbf", gamma=0.04) -X_reduced = rbf_pca.fit_transform(X) -Figure 8- - +Scikit-Learn’s PCA class implements PCA using SVD decomposition just like we did before. The +following code applies PCA to reduce the dimensionality of the dataset down to two dimensions (note +that it automatically takes care of centering the data):

    -

    -
    + +
    from sklearn.decomposition import PCA
+pca = PCA(n_components = 2)
+X2D = pca.fit_transform(X)
+
    +

    +After fitting the PCA transformer to the dataset, you can access the principal components using the +components variable (note that it contains the PCs as horizontal vectors, so, for example, the first +principal component is equal to +

    + + +

    pca.components_.T[:, 0]).
+
    +

    +Another very useful piece of information is the explained variance ratio of each principal component, +available via the \( explained\_variance\_ratio \) variable. It indicates the proportion of the dataset’s +variance that lies along the axis of each principal component. +More material to come here.

    @@ -126,6 +152,10 @@ Figure 8-

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    • diff --git a/doc/pub/DimRed/html/._DimRed-bs004.html b/doc/pub/DimRed/html/._DimRed-bs004.html index c5c4b293d..406b44c9d 100644 --- a/doc/pub/DimRed/html/._DimRed-bs004.html +++ b/doc/pub/DimRed/html/._DimRed-bs004.html @@ -45,13 +45,33 @@ Automatically generated HTML file from DocOnce source None, '___sec0'), ('Principal Component Analysis', 2, None, '___sec1'), - ('Kernel PCA', 2, None, '___sec2'), - ('LLE', 2, None, '___sec3'), - ('Other techniques', 2, None, '___sec4')]} + ('PCA and scikit-learn', 2, None, '___sec2'), + ('More on the PCA', 2, None, '___sec3'), + ('Incremental PCA', 2, None, '___sec4'), + ('Randomized PCA', 2, None, '___sec5'), + ('Kernel PCA', 2, None, '___sec6'), + ('LLE', 2, None, '___sec7'), + ('Other techniques', 2, None, '___sec8')]} end of tocinfo --> + + + + + + +
    @@ -71,9 +91,13 @@ end of tocinfo --> @@ -89,16 +113,31 @@ end of tocinfo --> -

    LLE

    - +

    More on the PCA

    +Instead of arbitrarily choosing the number of dimensions to reduce down to, it is generally preferable to +choose the number of dimensions that add up to a sufficiently large portion of the variance (e.g., 95%). +Unless, of course, you are reducing dimensionality for data visualization — in that case you will +generally want to reduce the dimensionality down to 2 or 3. +The following code computes PCA without reducing dimensionality, then computes the minimum number +of dimensions required to preserve 95% of the training set’s variance:

    -Locally Linear Embedding (LLE)8 is another very powerful nonlinear dimensionality reduction -(NLDR) technique. It is a Manifold Learning technique that does not rely on projections like the previous -algorithms. In a nutshell, LLE works by first measuring how each training instance linearly relates to its -closest neighbors (c.n.), and then looking for a low-dimensional representation of the training set where -these local relationships are best preserved (more details shortly). This makes it particularly good at -unrolling twisted manifolds, especially when there is not too much noise. + +

    pca = PCA()
+pca.fit(X)
+cumsum = np.cumsum(pca.explained_variance_ratio_)
+d = np.argmax(cumsum >= 0.95) + 1
+
    +

    +You could then set \( n\_components=d \) and run PCA again. However, there is a much better option: instead +of specifying the number of principal components you want to preserve, you can set \( n\_components \) to be +a float between 0.0 and 1.0, indicating the ratio of variance you wish to preserve: +

    + + +

    pca = PCA(n_components=0.95)
+X_reduced = pca.fit_transform(X)
+

    @@ -110,6 +149,10 @@ unrolling twisted manifolds, especially when there is not too much noise.

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    • diff --git a/doc/pub/DimRed/html/._DimRed-bs005.html b/doc/pub/DimRed/html/._DimRed-bs005.html index f5ac794f0..8b899a8b6 100644 --- a/doc/pub/DimRed/html/._DimRed-bs005.html +++ b/doc/pub/DimRed/html/._DimRed-bs005.html @@ -45,13 +45,33 @@ Automatically generated HTML file from DocOnce source None, '___sec0'), ('Principal Component Analysis', 2, None, '___sec1'), - ('Kernel PCA', 2, None, '___sec2'), - ('LLE', 2, None, '___sec3'), - ('Other techniques', 2, None, '___sec4')]} + ('PCA and scikit-learn', 2, None, '___sec2'), + ('More on the PCA', 2, None, '___sec3'), + ('Incremental PCA', 2, None, '___sec4'), + ('Randomized PCA', 2, None, '___sec5'), + ('Kernel PCA', 2, None, '___sec6'), + ('LLE', 2, None, '___sec7'), + ('Other techniques', 2, None, '___sec8')]} end of tocinfo --> + + + + + + +
    @@ -71,9 +91,13 @@ end of tocinfo --> @@ -89,25 +113,14 @@ end of tocinfo --> -

    Other techniques

    +

    Incremental PCA

    +One problem with the preceding implementation of PCA is that it requires the whole training set to fit in +memory in order for the SVD algorithm to run. Fortunately, Incremental PCA (IPCA) algorithms have +been developed: you can split the training set into mini-batches and feed an IPCA algorithm one minibatch +at a time. This is useful for large training sets, and also to apply PCA online (i.e., on the fly, as new +instances arrive).

    -There are many other dimensionality reduction techniques, several of which are available in Scikit-Learn. -Here are some of the most popular: -Multidimensional Scaling (MDS) reduces dimensionality while trying to preserve the distances -between the instances (see Figure 8-13). -Isomap creates a graph by connecting each instance to its nearest neighbors, then reduces -dimensionality while trying to preserve the geodesic distances9 between the instances. -t-Distributed Stochastic Neighbor Embedding (t-SNE) reduces dimensionality while trying to keep -similar instances close and dissimilar instances apart. It is mostly used for visualization, in -particular to visualize clusters of instances in high-dimensional space (e.g., to visualize the MNIST -images in 2D). -Linear Discriminant Analysis (LDA) is actually a classification algorithm, but during training it -learns the most discriminative axes between the classes, and these axes can then be used to define a -hyperplane onto which to project the data. The benefit is that the projection will keep classes as far -apart as possible, so LDA is a good technique to reduce dimensionality before running another -classification algorithm such as an SVM classifier -

      @@ -118,6 +131,11 @@ classification algorithm such as an SVM classifier
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    diff --git a/doc/pub/Regression/ipynb/Regression.ipynb b/doc/pub/Regression/ipynb/Regression.ipynb index 110c21294..f58c5845b 100644 --- a/doc/pub/Regression/ipynb/Regression.ipynb +++ b/doc/pub/Regression/ipynb/Regression.ipynb @@ -4509,14 +4509,14 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[-0. 4. -8. ... -4. -4. -0.]\n" + "[-0. 8. -0. ... -4. -0. -8.]\n" ] } ], @@ -4625,20 +4625,20 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[[ 1. -1. -1. ... -1. 1. 1.]\n", - " [ 1. 1. -1. ... -1. -1. 1.]\n", - " [ 1. -1. 1. ... -1. -1. 1.]\n", - " ...\n", - " [ 1. 1. -1. ... 1. -1. 1.]\n", + "[[ 1. 1. -1. ... -1. 1. 1.]\n", " [ 1. -1. -1. ... 1. 1. 1.]\n", - " [ 1. 1. 1. ... 1. -1. 1.]]\n" + " [ 1. 1. 1. ... -1. 1. 1.]\n", + " ...\n", + " [ 1. -1. 1. ... 1. 1. 1.]\n", + " [ 1. 1. 1. ... 1. -1. 1.]\n", + " [ 1. 1. -1. ... -1. -1. 1.]]\n" ] } ], @@ -4798,7 +4798,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -4808,8 +4808,8 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mLinAlgError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget_ols_weights_naive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m 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3\u001b[0m \u001b[0momega\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_ols_weights_naive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train_own\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget_ols_weights_naive\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mreturn\u001b[0m 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\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 975\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mLinAlgError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"singular matrix\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 976\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0minfo\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 977\u001b[0m raise ValueError('illegal value in %d-th argument of internal '\n", "\u001b[0;31mLinAlgError\u001b[0m: singular matrix" ] @@ -4907,7 +4907,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -4925,7 +4925,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -4943,7 +4943,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -4959,7 +4959,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -4976,12 +4976,12 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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    " ] @@ -5087,12 +5087,12 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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    " ] diff --git a/doc/src/DimRed/DimRed.do.txt b/doc/src/DimRed/DimRed.do.txt index 297acc510..e3981c5ca 100644 --- a/doc/src/DimRed/DimRed.do.txt +++ b/doc/src/DimRed/DimRed.do.txt @@ -13,8 +13,8 @@ solution, as we will see. This problem is often referred to as the curse of dime Fortunately, in real-world problems, it is often possible to reduce the number of features considerably, turning an intractable problem into a tractable one. -and we will go through three of the most popular dimensionality -reduction techniques: PCA, Kernel PCA, and LLE. +Here we will discuss some of the most popular dimensionality +reduction techniques: the principal component analysis PCA, Kernel PCA, and Locally Linear Embedding (LLE). !eblock @@ -26,126 +26,86 @@ reduction techniques: PCA, Kernel PCA, and LLE. Principal Component Analysis (PCA) is by far the most popular dimensionality reduction algorithm. First it identifies the hyperplane that lies closest to the data, and then it projects the data onto it. -The following Python code uses NumPy’s svd() function to obtain all the principal components of the -training set, then extracts the first two PCs: +The following Python code uses NumPy’s _svd()_ function to obtain all the principal components of the +training set, then extracts the first two principal components +!bc pycod X_centered = X - X.mean(axis=0) U, s, V = np.linalg.svd(X_centered) c1 = V.T[:, 0] c2 = V.T[:, 1] +!ec - -PCA assumes that the dataset is centered around the origin. As we will see, Scikit-Learn’s PCA classes take care of centering +PCA assumes that the dataset is centered around the origin. Scikit-Learn’s PCA classes take care of centering the data for you. However, if you implement PCA yourself (as in the preceding example), or if you use other libraries, don’t forget to center the data first. Once you have identified all the principal components, you can reduce the dimensionality of the dataset -down to d dimensions by projecting it onto the hyperplane defined by the first d principal components. -Selecting this hyperplane ensures that the projection will preserve as much variance as possible. For -example, in Figure 8-2 the 3D dataset is projected down to the 2D plane defined by the first two principal -components, preserving a large part of the dataset’s variance. As a result, the 2D projection looks very -much like the original 3D dataset. - +down to $d$ dimensions by projecting it onto the hyperplane defined by the first $d$ principal components. +Selecting this hyperplane ensures that the projection will preserve as much variance as possible. +!bc pycod W2 = V.T[:, :2] X2D = X_centered.dot(W2) +!ec + +!split +===== PCA and scikit-learn ===== Scikit-Learn’s PCA class implements PCA using SVD decomposition just like we did before. The following code applies PCA to reduce the dimensionality of the dataset down to two dimensions (note that it automatically takes care of centering the data): +!bc pycod from sklearn.decomposition import PCA pca = PCA(n_components = 2) X2D = pca.fit_transform(X) +!ec After fitting the PCA transformer to the dataset, you can access the principal components using the -components_ variable (note that it contains the PCs as horizontal vectors, so, for example, the first -principal component is equal to pca.components_.T[:, 0]). - +components variable (note that it contains the PCs as horizontal vectors, so, for example, the first +principal component is equal to +!bc pycod +pca.components_.T[:, 0]). +!ec Another very useful piece of information is the explained variance ratio of each principal component, -available via the explained_variance_ratio_ variable. It indicates the proportion of the dataset’s -variance that lies along the axis of each principal component. For example, let’s look at the explained -variance ratios of the first two components of the 3D dataset represented in Figure 8-2: ->>> print(pca.explained_variance_ratio_) -array([ 0.84248607, 0.14631839]) -This tells you that 84.2% of the dataset’s variance lies along the first axis, and 14.6% lies along the -second axis. This leaves less than 1.2% for the third axis, so it is reasonable to assume that it probably -carries little information. - +available via the $explained\_variance\_ratio$ variable. It indicates the proportion of the dataset’s +variance that lies along the axis of each principal component. +More material to come here. +!split +===== More on the PCA ===== Instead of arbitrarily choosing the number of dimensions to reduce down to, it is generally preferable to choose the number of dimensions that add up to a sufficiently large portion of the variance (e.g., 95%). Unless, of course, you are reducing dimensionality for data visualization — in that case you will generally want to reduce the dimensionality down to 2 or 3. The following code computes PCA without reducing dimensionality, then computes the minimum number of dimensions required to preserve 95% of the training set’s variance: +!bc pycod pca = PCA() pca.fit(X) cumsum = np.cumsum(pca.explained_variance_ratio_) d = np.argmax(cumsum >= 0.95) + 1 -You could then set n_components=d and run PCA again. However, there is a much better option: instead -of specifying the number of principal components you want to preserve, you can set n_components to be +!ec +You could then set $n\_components=d$ and run PCA again. However, there is a much better option: instead +of specifying the number of principal components you want to preserve, you can set $n\_components$ to be a float between 0.0 and 1.0, indicating the ratio of variance you wish to preserve: +!bc pycod pca = PCA(n_components=0.95) X_reduced = pca.fit_transform(X) +!ec - - -Obviously after dimensionality reduction, the training set takes up much less space. For example, try -applying PCA to the MNIST dataset while preserving 95% of its variance. You should find that each -instance will have just over 150 features, instead of the original 784 features. So while most of the -variance is preserved, the dataset is now less than 20% of its original size! This is a reasonable -compression ratio, and you can see how this can speed up a classification algorithm (such as an SVM -classifier) tremendously. -It is also possible to decompress the reduced dataset back to 784 dimensions by applying the inverse -transformation of the PCA projection. Of course this won’t give you back the original data, since the -projection lost a bit of information (within the 5% variance that was dropped), but it will likely be quite -close to the original data. The mean squared distance between the original data and the reconstructed data -(compressed and then decompressed) is called the reconstruction error. For example, the following code -compresses the MNIST dataset down to 154 dimensions, then uses the inverse_transform() method to -decompress it back to 784 dimensions. Figure 8-9 shows a few digits from the original training set (on the -left), and the corresponding digits after compression and decompression. You can see that there is a slight -image quality loss, but the digits are still mostly intact. -pca = PCA(n_components = 154) -X_mnist_reduced = pca.fit_transform(X_mnist) -X_mnist_recovered = pca.inverse_transform(X_mnist_reduced) -Figure - - - -Incremental PCA +!split +===== Incremental PCA ===== One problem with the preceding implementation of PCA is that it requires the whole training set to fit in memory in order for the SVD algorithm to run. Fortunately, Incremental PCA (IPCA) algorithms have been developed: you can split the training set into mini-batches and feed an IPCA algorithm one minibatch at a time. This is useful for large training sets, and also to apply PCA online (i.e., on the fly, as new instances arrive). -The following code splits the MNIST dataset into 100 mini-batches (using NumPy’s array_split() -function) and feeds them to Scikit-Learn’s IncrementalPCA class5 to reduce the dimensionality of the -MNIST dataset down to 154 dimensions (just like before). Note that you must call the partial_fit() -method with each mini-batch rather than the fit() method with the whole training set: -from sklearn.decomposition import IncrementalPCA -n_batches = 100 -inc_pca = IncrementalPCA(n_components=154) -for X_batch in np.array_split(X_mnist, n_batches): -inc_pca.partial_fit(X_batch) -X_mnist_reduced = inc_pca.transform(X_mnist) +!split +===== Randomized PCA ===== - -Alternatively, you can use NumPy’s memmap class, which allows you to manipulate a large array stored in -a binary file on disk as if it were entirely in memory; the class loads only the data it needs in memory, -when it needs it. Since the IncrementalPCA class uses only a small part of the array at any given time, -the memory usage remains under control. This makes it possible to call the usual fit() method, as you -can see in the following code: -X_mm = np.memmap(filename, dtype="float32", mode="readonly", shape=(m, n)) -batch_size = m // n_batches -inc_pca = IncrementalPCA(n_components=154, batch_size=batch_size) -inc_pca.fit(X_mm) - - -Randomized PCA Scikit-Learn offers yet another option to perform PCA, called Randomized PCA. This is a stochastic algorithm that quickly finds an approximation of the first d principal components. Its computational -complexity is O(m × d2) + O(d3), instead of O(m × n2) + O(n3), so it is dramatically faster than the -previous algorithms when d is much smaller than n. -rnd_pca = PCA(n_components=154, svd_solver="randomized") -X_reduced = rnd_pca.fit_transform(X_mnist) +complexity is $O(m \times d^2)+O(d^3)$, instead of $O(m \times n^2) + O(n^3)$, so it is dramatically faster than the +previous algorithms when $d$ is much smaller than $n$. !eblock @@ -155,7 +115,6 @@ X_reduced = rnd_pca.fit_transform(X_mnist) ===== Kernel PCA ===== !bblock -Kernel PCA The kernel trick is a mathematical technique that implicitly maps instances into a very high-dimensional space (called the feature space), enabling nonlinear classification and regression with Support Vector Machines. Recall that a linear decision boundary in the high-dimensional feature @@ -165,10 +124,11 @@ projections for dimensionality reduction. This is called Kernel PCA (kPCA). It i preserving clusters of instances after projection, or sometimes even unrolling datasets that lie close to a twisted manifold. For example, the following code uses Scikit-Learn’s KernelPCA class to perform kPCA with an +!bc pycod from sklearn.decomposition import KernelPCA rbf_pca = KernelPCA(n_components = 2, kernel="rbf", gamma=0.04) X_reduced = rbf_pca.fit_transform(X) -Figure 8- +!ec !eblock @@ -176,12 +136,11 @@ Figure 8- !split ===== LLE ===== -Locally Linear Embedding (LLE)8 is another very powerful nonlinear dimensionality reduction +Locally Linear Embedding (LLE) is another very powerful nonlinear dimensionality reduction (NLDR) technique. It is a Manifold Learning technique that does not rely on projections like the previous algorithms. In a nutshell, LLE works by first measuring how each training instance linearly relates to its closest neighbors (c.n.), and then looking for a low-dimensional representation of the training set where -these local relationships are best preserved (more details shortly). This makes it particularly good at -unrolling twisted manifolds, especially when there is not too much noise. +these local relationships are best preserved (more details shortly). @@ -190,17 +149,9 @@ unrolling twisted manifolds, especially when there is not too much noise. There are many other dimensionality reduction techniques, several of which are available in Scikit-Learn. + Here are some of the most popular: -Multidimensional Scaling (MDS) reduces dimensionality while trying to preserve the distances -between the instances (see Figure 8-13). -Isomap creates a graph by connecting each instance to its nearest neighbors, then reduces -dimensionality while trying to preserve the geodesic distances9 between the instances. -t-Distributed Stochastic Neighbor Embedding (t-SNE) reduces dimensionality while trying to keep -similar instances close and dissimilar instances apart. It is mostly used for visualization, in -particular to visualize clusters of instances in high-dimensional space (e.g., to visualize the MNIST -images in 2D). -Linear Discriminant Analysis (LDA) is actually a classification algorithm, but during training it -learns the most discriminative axes between the classes, and these axes can then be used to define a -hyperplane onto which to project the data. The benefit is that the projection will keep classes as far -apart as possible, so LDA is a good technique to reduce dimensionality before running another -classification algorithm such as an SVM classifier +* _Multidimensional Scaling (MDS)_ reduces dimensionality while trying to preserve the distances between the instances. +* _Isomap_ creates a graph by connecting each instance to its nearest neighbors, then reduces dimensionality while trying to preserve the geodesic distances between the instances. +* _t-Distributed Stochastic Neighbor Embedding_ (t-SNE) reduces dimensionality while trying to keep similar instances close and dissimilar instances apart. It is mostly used for visualization, in particular to visualize clusters of instances in high-dimensional space (e.g., to visualize the MNIST images in 2D). +* Linear Discriminant Analysis (LDA) is actually a classification algorithm, but during training it learns the most discriminative axes between the classes, and these axes can then be used to define a hyperplane onto which to project the data. The benefit is that the projection will keep classes as far apart as possible, so LDA is a good technique to reduce dimensionality before running another classification algorithm such as a Support Vector Machine (SVM) classifier discussed in the SVM lectures. diff --git a/doc/web/course.do.txt b/doc/web/course.do.txt index e2aec2115..c6af03548 100644 --- a/doc/web/course.do.txt +++ b/doc/web/course.do.txt @@ -250,23 +250,23 @@ Acronyms for textbooks and references to chapter |----------------------------------------------------------------------------------------------------------------------------| | Week 38 | Optimization methods | Exercises and project 1 | HTF chapter 5 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/Splines/html/Splines-bs.html" | Work on project 1, deadline October 1| |----------------------------------------------------------------------------------------------------------------------------| -| Week 39 | Statistics, Bayesian statistics | Project 1 | "Lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/Bayesian/html/Bayesian-bs.html" | Work on Project 1| +| Week 39 | Logistic regression and optimization | Project 1 | "Lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/Bayesian/html/Bayesian-bs.html" | Work on Project 1| |----------------------------------------------------------------------------------------------------------------------------| -| Week 40 | Statistics, Monte Carlo and Randow walks | Presentation of project 2, deadline November 5 | "Lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/Statistics/html/Statistics-bs.html" | Deadline project 1, October 1| +| Week 40 | Neural Networks | Presentation of project 2, deadline November 5 | "Lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/Statistics/html/Statistics-bs.html" | Deadline project 1, October 1| |----------------------------------------------------------------------------------------------------------------------------| -| Week 41 | Statistics, Monte Carlo, Gibbs and Metropolis sampling | Project 2 | "Lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/Statistics/html/Statistics-bs.html" | Work on project 2 | +| Week 41 | Neural Networks | Project 2 | "Lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/Statistics/html/Statistics-bs.html" | Work on project 2 | |----------------------------------------------------------------------------------------------------------------------------| -| Week 42 | Neural networks | Project 2 | HTF chapter 11 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/NeuralNet/html/NeuralNet-bs.html" | Work on project 2 | +| Week 42 | Neural Networks | Project 2 | HTF chapter 11 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/NeuralNet/html/NeuralNet-bs.html" | Work on project 2 | |----------------------------------------------------------------------------------------------------------------------------| -| Week 43 | Neural networks | Project 2 | HTF chapter 11 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/NeuralNet/html/NeuralNet-bs.html" | Work on project 2 | +| Week 43 | Dimensionality reduction and support vector machines | Project 2 | HTF chapters 3 and 12 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/NeuralNet/html/NeuralNet-bs.html" | Work on project 2 | |----------------------------------------------------------------------------------------------------------------------------| -| Week 44 | Neural networks | Project 2 | HTF chapter 11 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/NeuralNet/html/NeuralNet-bs.html" | Work on project 2 | +| Week 44 | SVM and tree and forest models | Project 2 | HTF chapter 9 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/NeuralNet/html/NeuralNet-bs.html" | Work on project 2 | |----------------------------------------------------------------------------------------------------------------------------| -| Week 45 | Support Vector Machines | Presentation and discussion of project 3 | HTF chapter 12 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/svm/html/svm-bs.html" | Deadline project 2 November 5 | +| Week 45 | Unsupervised learning, Boltzmann machines | Presentation and discussion of project 3 | HTF chapter 14 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/BM/html/BM-bs.html" | Deadline project 2 November 5 | |----------------------------------------------------------------------------------------------------------------------------| -| Week 46 | Decision trees | Project 3 | HTF chapter 9 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/DecisionTrees/html/DecisionTrees-bs.html" | Work on project 3 | +| Week 46 | Bayesian statitics | Project 3 | TBA | Work on project 3 | |----------------------------------------------------------------------------------------------------------------------------| -| Week 47 | Unsupervised learning, Boltzmann machines | Project 3 | HTF chapter 14 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/BM/html/BM-bs.html" | Work on project 3 | +| Week 47 | Bayesian statistics | Project 3 | TBA | Work on project 3 | |----------------------------------------------------------------------------------------------------------------------------| -| Week 48 | Unsupervised learning, summary of course and final workshop | Project 3 | Lecture notes | Final workshop with presentation of project 3 | +| Week 48 | Summary of course and final workshop | Project 3 | Lecture notes | Final workshop with presentation of project 3 on November 30| |----------------------------------------------------------------------------------------------------------------------------| diff --git a/doc/web/course.html b/doc/web/course.html index 8a0677593..e5736aadc 100644 --- a/doc/web/course.html +++ b/doc/web/course.html @@ -836,24 +836,24 @@ Acronyms for textbooks and references to chapter - + - - - - - - - - - - - - - - - + + + + + + + + + + + + + + +
    Week and days Topics to be covered Projects, exercises and deadlines Reading assignments Lab activities
    Week and days Topics to be covered Projects, exercises and deadlines Reading assignments Lab activities
    Week 34 Introduction and regression analysis Exercises TBD HTF chapters 1-3 and lecture notes No lab first week
    Week 35 Regression analysis Exercises TBD HTF chapter 3 and lecture notes Introduction to Git, GitHub and Python software, Python technicalities and work on exercises
    Week 36 Regression analysis and nearest neighbors Exercises TBD HTF chapters 3, 4 and 13 and lecture notes Work on exercises
    Week 37 Classification and logistic regression Presentation of Project 1, deadline October 1 HTF chapter 4 and lecture notes Work on project 1
    Week 38 Optimization methods Exercises and project 1 HTF chapter 5 and lecture notes Work on project 1, deadline October 1
    Week 39 Statistics, Bayesian statistics Project 1 Lecture notes Work on Project 1
    Week 40 Statistics, Monte Carlo and Randow walks Presentation of project 2, deadline November 5 Lecture notes Deadline project 1, October 1
    Week 41 Statistics, Monte Carlo, Gibbs and Metropolis sampling Project 2 Lecture notes Work on project 2
    Week 42 Neural networks Project 2 HTF chapter 11 and lecture notes Work on project 2
    Week 43 Neural networks Project 2 HTF chapter 11 and lecture notes Work on project 2
    Week 44 Neural networks Project 2 HTF chapter 11 and lecture notes Work on project 2
    Week 45 Support Vector Machines Presentation and discussion of project 3 HTF chapter 12 and lecture notes Deadline project 2 November 5
    Week 46 Decision trees Project 3 HTF chapter 9 and lecture notes Work on project 3
    Week 47 Unsupervised learning, Boltzmann machines Project 3 HTF chapter 14 and lecture notes Work on project 3
    Week 48 Unsupervised learning, summary of course and final workshop Project 3 Lecture notes Final workshop with presentation of project 3
    Week 34 Introduction and regression analysis Exercises TBD HTF chapters 1-3 and lecture notes No lab first week
    Week 35 Regression analysis Exercises TBD HTF chapter 3 and lecture notes Introduction to Git, GitHub and Python software, Python technicalities and work on exercises
    Week 36 Regression analysis and nearest neighbors Exercises TBD HTF chapters 3, 4 and 13 and lecture notes Work on exercises
    Week 37 Classification and logistic regression Presentation of Project 1, deadline October 1 HTF chapter 4 and lecture notes Work on project 1
    Week 38 Optimization methods Exercises and project 1 HTF chapter 5 and lecture notes Work on project 1, deadline October 1
    Week 39 Logistic regression and optimization Project 1 Lecture notes Work on Project 1
    Week 40 Neural Networks Presentation of project 2, deadline November 5 Lecture notes Deadline project 1, October 1
    Week 41 Neural Networks Project 2 Lecture notes Work on project 2
    Week 42 Neural Networks Project 2 HTF chapter 11 and lecture notes Work on project 2
    Week 43 Dimensionality reduction and support vector machines Project 2 HTF chapters 3 and 12 and lecture notes Work on project 2
    Week 44 SVM and tree and forest models Project 2 HTF chapter 9 and lecture notes Work on project 2
    Week 45 Unsupervised learning, Boltzmann machines Presentation and discussion of project 3 HTF chapter 14 and lecture notes Deadline project 2 November 5
    Week 46 Bayesian statitics Project 3 TBA Work on project 3
    Week 47 Bayesian statistics Project 3 TBA Work on project 3
    Week 48 Summary of course and final workshop Project 3 Lecture notes Final workshop with presentation of project 3 on November 30