update on intro
This commit is contained in:
mhjensen
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# import necessary packages
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import numpy as np
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import matplotlib.pyplot as plt
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from sklearn import datasets
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# ensure the same random numbers appear every time
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np.random.seed(0)
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# display images in notebook
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plt.rcParams['figure.figsize'] = (12,12)
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# download MNIST dataset
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digits = datasets.load_digits()
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# define inputs and labels
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inputs = digits.images
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labels = digits.target
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# RGB images have a depth of 3
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# our images are grayscale so they should have a depth of 1
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inputs = inputs[:,:,:,np.newaxis]
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print("inputs = (n_inputs, pixel_width, pixel_height, depth) = " + str(inputs.shape))
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print("labels = (n_inputs) = " + str(labels.shape))
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# choose some random images to display
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n_inputs = len(inputs)
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indices = np.arange(n_inputs)
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random_indices = np.random.choice(indices, size=5)
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for i, image in enumerate(digits.images[random_indices]):
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plt.subplot(1, 5, i+1)
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plt.axis('off')
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plt.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest')
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plt.title("Label: %d" % digits.target[random_indices[i]])
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plt.show()
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from keras.utils import to_categorical
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from sklearn.model_selection import train_test_split
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# representation of labels
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labels = to_categorical(labels)
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# split into train and test data
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# one-liner from scikit-learn library
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train_size = 0.8
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test_size = 1 - train_size
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X_train, X_test, Y_train, Y_test = train_test_split(inputs, labels, train_size=train_size,
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test_size=test_size)
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import tensorflow as tf
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from keras.models import Sequential
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from keras.layers.convolutional import Conv2D
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from keras.layers.convolutional import MaxPooling2D
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from keras.layers import Flatten
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from keras.layers import Dense
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from keras.regularizers import l2
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from keras.optimizers import SGD
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def create_convolutional_neural_network_keras(input_shape, receptive_field,
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n_filters, n_neurons_connected, n_categories,
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eta, lmbd):
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model = Sequential()
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model.add(Conv2D(n_filters, (receptive_field, receptive_field), input_shape=input_shape, padding='same',
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activation='relu', kernel_regularizer=l2(lmbd)))
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model.add(MaxPooling2D(pool_size=(2, 2)))
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model.add(Flatten())
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model.add(Dense(n_neurons_connected, activation='relu', kernel_regularizer=l2(lmbd)))
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model.add(Dense(n_categories, activation='softmax', kernel_regularizer=l2(lmbd)))
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sgd = SGD(lr=eta)
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model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
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return model
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epochs = 100
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batch_size = 100
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input_shape = X_train.shape[1:4]
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receptive_field = 3
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n_filters = 10
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n_neurons_connected = 50
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n_categories = 10
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eta_vals = np.logspace(-5, 1, 7)
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lmbd_vals = np.logspace(-5, 1, 7)
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CNN_keras = np.zeros((len(eta_vals), len(lmbd_vals)), dtype=object)
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for i, eta in enumerate(eta_vals):
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for j, lmbd in enumerate(lmbd_vals):
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CNN = create_convolutional_neural_network_keras(input_shape, receptive_field,
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n_filters, n_neurons_connected, n_categories,
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eta, lmbd)
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CNN.fit(X_train, Y_train, epochs=epochs, batch_size=batch_size, verbose=0)
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scores = CNN.evaluate(X_test, Y_test)
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CNN_keras[i][j] = CNN
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print("Learning rate = ", eta)
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print("Lambda = ", lmbd)
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print("Test accuracy: %.3f" % scores[1])
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print()
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# visual representation of grid search
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# uses seaborn heatmap, could probably do this in matplotlib
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import seaborn as sns
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sns.set()
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train_accuracy = np.zeros((len(eta_vals), len(lmbd_vals)))
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test_accuracy = np.zeros((len(eta_vals), len(lmbd_vals)))
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for i in range(len(eta_vals)):
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for j in range(len(lmbd_vals)):
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CNN = CNN_keras[i][j]
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train_accuracy[i][j] = CNN.evaluate(X_train, Y_train)[1]
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test_accuracy[i][j] = CNN.evaluate(X_test, Y_test)[1]
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fig, ax = plt.subplots(figsize = (10, 10))
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sns.heatmap(train_accuracy, annot=True, ax=ax, cmap="viridis")
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ax.set_title("Training Accuracy")
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ax.set_ylabel("$\eta$")
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ax.set_xlabel("$\lambda$")
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plt.show()
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fig, ax = plt.subplots(figsize = (10, 10))
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sns.heatmap(test_accuracy, annot=True, ax=ax, cmap="viridis")
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ax.set_title("Test Accuracy")
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ax.set_ylabel("$\eta$")
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ax.set_xlabel("$\lambda$")
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plt.show()
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||||
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|
||||
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34.455,110.1,0.924,0.944999979,0.990684,0.96004,0.933675,0.909954,0.888876,0.869645,0.852262,0.837569,0.825567,0.813187,1.32312,1.30955,1.29881,1.29046,1.28449,1.27727,1.26879,1.26178,1.25624,1.2509,1.18012,1.19925,1.22136,1.24733,1.27716,1.30684,1.33635,1.36462,1.39165,1.43209,1.47903,1.51424,1.53848,0.00164325,0.00178025,0.00193075,0.0020975,0.0022805,0.00248375,0.00270725,0.002963,0.003251,0.0035655,0.0020845,0.00230275,0.00254425,0.00281325,0.00310975,0.00343575,0.00379125,0.00418475,0.00461625,0.005109,0.00152831,0.00161865,0.00180814,0.00205033,0.00234523,0.00268458,0.00306838,0.00349876,0.00397571,0.00481281,0.00614197,0.0077635,0.00923608
|
||||
34.043,50.7,0.644,0.81499989,1.10772,1.08116,1.05423,1.03135,1.01249,0.99311,0.973197,0.956625,0.943395,0.927993,1.46048,1.4625,1.46645,1.47171,1.47826,1.48303,1.48602,1.49102,1.49801,1.50417,1.17723,1.19001,1.20708,1.23,1.25877,1.28843,1.31899,1.35343,1.39175,1.44396,1.51065,1.57356,1.62141,0.0018765,0.0020575,0.0022545,0.00248025,0.00273475,0.00301875,0.00333225,0.00369475,0.00410625,0.00456275,0.00242925,0.00273325,0.00307575,0.00346925,0.00391375,0.004411,0.004961,0.00559725,0.00631975,0.0071395,0.00159872,0.00167436,0.00185024,0.00208632,0.00238259,0.00273397,0.00314044,0.00362322,0.0041823,0.00517889,0.00684787,0.00903062,0.0111141
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||||
26.215,102.9,0.78,0.844999979,0.897285,0.863974,0.836752,0.81252,0.791278,0.772002,0.754691,0.739764,0.727218,0.716435,1.16868,1.14156,1.11812,1.09904,1.08433,1.07096,1.05894,1.0473,1.03605,1.02734,1.17248,1.19135,1.21118,1.23338,1.25796,1.28122,1.30318,1.32165,1.33662,1.36185,1.39547,1.42031,1.43405,0.00152575,0.00163875,0.00176825,0.00191325,0.00207375,0.002254,0.002454,0.0026825,0.0029395,0.003235,0.00186625,0.0020285,0.0022095,0.002415,0.002645,0.0029025,0.0031875,0.00350575,0.00385725,0.00426,0.00160269,0.00168615,0.00186715,0.00209793,0.00237852,0.00269658,0.00305213,0.00344609,0.00387846,0.0046449,0.00588652,0.00742075,0.00880391
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||||
27.863,81.3,0.684,0.67499999,0.935704,0.895263,0.860392,0.828773,0.800406,0.77565,0.754507,0.733421,0.712393,0.696175,1.2255,1.19174,1.16223,1.13578,1.11239,1.08962,1.06747,1.04598,1.02517,1.00707,1.18134,1.19927,1.22091,1.24292,1.26529,1.28783,1.31055,1.33174,1.35141,1.38062,1.41,1.43289,1.44659,0.00161225,0.00171975,0.00184125,0.00197625,0.00212475,0.00229625,0.00249075,0.00270075,0.00292625,0.00320125,0.00198625,0.00214825,0.00232675,0.00252575,0.00274525,0.002988,0.003254,0.0035435,0.0038565,0.0042145,0.00165523,0.00173608,0.00192051,0.00215152,0.00242909,0.00274988,0.00311388,0.00352215,0.0039747,0.00477063,0.00602912,0.00759797,0.00900774
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||||
27.039,93.9,0.66,0.984999914,0.970407,0.940277,0.916194,0.894327,0.874673,0.857249,0.842055,0.827939,0.814902,0.805633,1.25397,1.24088,1.23479,1.23179,1.23187,1.23237,1.23328,1.23488,1.23716,1.24453,1.16826,1.18234,1.20118,1.22124,1.24253,1.26643,1.29295,1.32024,1.34829,1.39337,1.45146,1.50515,1.54492,0.001699,0.0018465,0.0020155,0.00220775,0.00242325,0.00266825,0.00294275,0.00325475,0.00360425,0.0040205,0.002089,0.00231675,0.00258025,0.002883,0.003225,0.00361275,0.00404625,0.00454375,0.00510525,0.00577975,0.00164354,0.00171613,0.00189519,0.0021259,0.00240827,0.00274519,0.00313668,0.0035893,0.00410304,0.00504336,0.00660103,0.00862816,0.0105479
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||||
33.013,101.1,0.852,1.065000245,1.0109,0.983213,0.960695,0.94098,0.924067,0.910037,0.898889,0.889082,0.880617,0.872387,1.33896,1.33317,1.33173,1.33277,1.33627,1.34146,1.34834,1.35429,1.35933,1.36636,1.17469,1.19232,1.21553,1.23968,1.26477,1.29331,1.3253,1.35645,1.38675,1.43162,1.48728,1.53358,1.5664,0.0017045,0.0018585,0.0020315,0.002225,0.002439,0.00268175,0.00295325,0.00326125,0.00360575,0.00398925,0.0021415,0.00238725,0.00266375,0.002978,0.00333,0.00372825,0.00417275,0.0046685,0.0052155,0.00585125,0.00154136,0.00162633,0.00181618,0.00205674,0.00234802,0.00269283,0.00309119,0.00354277,0.0040476,0.0049417,0.00638282,0.00817651,0.00982853
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||||
31.571,38.1,0.764,0.754999993,1.06613,1.03002,0.995255,0.961843,0.929783,0.901983,0.878441,0.854928,0.831441,0.81065,1.41838,1.4001,1.38116,1.36303,1.3457,1.32594,1.30374,1.28389,1.26639,1.24972,1.18682,1.20255,1.22339,1.24719,1.27395,1.30194,1.33117,1.36002,1.38849,1.43299,1.47737,1.51289,1.54189,0.00178275,0.00193025,0.00208875,0.00225825,0.00243875,0.002646,0.00288,0.0031335,0.0034065,0.00371575,0.00224775,0.0024825,0.0027355,0.003013,0.003315,0.00363675,0.00397825,0.00436325,0.00479175,0.0052745,0.00154537,0.00162957,0.00181523,0.00205415,0.00234631,0.00268768,0.00307825,0.00352279,0.0040213,0.00490967,0.00629291,0.00802616,0.00963066
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||||
30.953,70.5,0.652,1.075000262,1.07979,1.05635,1.03618,1.01648,0.997264,0.98051,0.966221,0.953969,0.943755,0.934708,1.40813,1.41366,1.42187,1.43279,1.44642,1.46281,1.48196,1.50244,1.52424,1.54021,1.17208,1.18334,1.19985,1.21947,1.24221,1.27062,1.3047,1.33874,1.37272,1.43056,1.51329,1.59533,1.64795,0.0018435,0.00203025,0.00224075,0.0024755,0.0027345,0.0030315,0.0033665,0.0037555,0.0041985,0.004707,0.002311,0.002612,0.002954,0.00335025,0.00380075,0.00433125,0.00494175,0.00565125,0.00645975,0.007366,0.00157791,0.00165038,0.0018247,0.00205369,0.00233734,0.00268595,0.00309952,0.00358004,0.00412751,0.00515112,0.00691187,0.00927138,0.0114633
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||||
34.661,68.7,0.844,0.934999645,1.06687,1.03851,1.01148,0.987597,0.966865,0.946295,0.92589,0.908161,0.893107,0.880923,1.4297,1.42739,1.42547,1.42306,1.42017,1.41847,1.41794,1.4175,1.41714,1.41942,1.18278,1.19952,1.22079,1.24606,1.27533,1.30691,1.34079,1.37515,1.40999,1.45533,1.51573,1.57412,1.61147,0.00178675,0.001952,0.00213,0.00232925,0.00254975,0.0027885,0.0030455,0.0033405,0.0036735,0.00405375,0.002277,0.00254925,0.00284775,0.0031785,0.0035415,0.0039485,0.0043995,0.00490375,0.00546125,0.006108,0.0015204,0.00160748,0.00179583,0.00204085,0.00234253,0.00269863,0.00310917,0.00357819,0.00410571,0.00502044,0.00652048,0.00843206,0.0101689
|
||||
26.627,88.5,0.676,0.714999978,0.917462,0.880085,0.846724,0.818363,0.795001,0.77108,0.746599,0.727724,0.714455,0.700722,1.19218,1.16246,1.13701,1.11386,1.09299,1.07353,1.05547,1.04094,1.02995,1.01652,1.17842,1.19587,1.21499,1.23569,1.25798,1.27946,1.30013,1.32147,1.34349,1.36825,1.40349,1.43615,1.45078,0.001596,0.0017075,0.0018305,0.0019735,0.0021365,0.002312,0.0025,0.0027265,0.0029915,0.003284,0.00195025,0.002116,0.0023,0.00250575,0.00273325,0.0029865,0.0032655,0.0035875,0.0039525,0.004351,0.0016703,0.00174897,0.00192898,0.00215774,0.00243524,0.0027551,0.00311733,0.00352967,0.00399214,0.00478204,0.00609215,0.00774999,0.00922696
|
||||
30.335,115.5,0.62,1.044999534,0.983645,0.955245,0.930963,0.909817,0.891806,0.875916,0.862147,0.850953,0.842334,0.833427,1.27088,1.26232,1.25959,1.25995,1.26339,1.26897,1.27669,1.28514,1.29431,1.3062,1.16722,1.17833,1.19408,1.21444,1.23939,1.26548,1.29268,1.32206,1.35361,1.40122,1.46516,1.52361,1.56756,0.00173225,0.00188725,0.00206175,0.0022635,0.0024925,0.0027555,0.0030525,0.00340225,0.00380475,0.00426,0.00213725,0.002381,0.002663,0.0029895,0.0033605,0.0037895,0.0042765,0.0048465,0.0054995,0.00626675,0.00166331,0.00172954,0.00190139,0.00213157,0.00242007,0.00276249,0.00315882,0.00362372,0.00415718,0.00512932,0.00676876,0.00893146,0.011014
|
||||
28.893,119.1,0.988,0.735000253,0.893324,0.850249,0.813204,0.777651,0.743587,0.713735,0.688094,0.662889,0.638122,0.615241,1.16409,1.12337,1.08557,1.05027,1.01746,0.984557,0.951559,0.918478,0.885315,0.857534,1.1672,1.19021,1.21495,1.23837,1.26048,1.28235,1.30399,1.32167,1.3354,1.36004,1.38126,1.38652,1.39411,0.001372,0.0014425,0.0015215,0.00160475,0.00169225,0.0017925,0.0019055,0.00202525,0.00215175,0.002292,0.001667,0.00177,0.00188,0.001998,0.002124,0.00225525,0.00239175,0.00253375,0.00268125,0.00285525,0.00147622,0.00156739,0.00174143,0.00194738,0.00218523,0.00245292,0.00275044,0.00307164,0.00341653,0.00402487,0.00494414,0.00601287,0.00698649
|
||||
30.129,83.1,0.748,0.645000084,0.956119,0.912382,0.87198,0.835878,0.804079,0.774989,0.74861,0.723229,0.698846,0.677756,1.26425,1.22461,1.18471,1.15009,1.12073,1.09182,1.06335,1.03554,1.00839,0.981991,1.18697,1.20731,1.22952,1.2527,1.27684,1.30032,1.32312,1.34275,1.35922,1.38538,1.41492,1.43767,1.44889,0.00159125,0.001687,0.001789,0.001904,0.002032,0.00217225,0.00232475,0.0024915,0.0026725,0.00288225,0.001971,0.00211525,0.00226575,0.00243475,0.00262225,0.002823,0.003037,0.00327,0.003522,0.003793,0.00160591,0.00169184,0.00187416,0.00210135,0.00237342,0.00268309,0.00303038,0.00341171,0.00382708,0.0045571,0.00570072,0.00710469,0.00834172
|
||||
31.777,92.1,0.828,1.035000114,1.01234,0.985333,0.96149,0.941373,0.924983,0.907882,0.890072,0.877653,0.870626,0.864797,1.33774,1.33056,1.32889,1.32715,1.32533,1.33002,1.34121,1.34669,1.34646,1.34966,1.17491,1.19261,1.21443,1.23973,1.26851,1.29594,1.32202,1.35094,1.38271,1.42161,1.48654,1.54056,1.56074,0.00171425,0.00187,0.002042,0.00223675,0.00245425,0.00269,0.002944,0.00324775,0.00360125,0.00400275,0.00214975,0.00239575,0.00267525,0.00298425,0.00332275,0.0037235,0.0041865,0.00468525,0.00521975,0.0058395,0.00154936,0.00163473,0.00182285,0.00206668,0.00236623,0.00270941,0.00309621,0.00354575,0.00405802,0.00493282,0.00643339,0.00828926,0.00989812
|
||||
35.485,57.9,0.916,1.084999917,1.09995,1.07566,1.05306,1.03345,1.01682,1.0037,0.994088,0.981833,0.966931,0.956302,1.47387,1.48086,1.48957,1.49826,1.50692,1.5175,1.53001,1.54223,1.55416,1.56691,1.17958,1.1961,1.21843,1.24398,1.27275,1.30495,1.34057,1.3773,1.41512,1.46627,1.5257,1.58946,1.63863,0.00180525,0.0019865,0.0021835,0.00240325,0.00264575,0.00292475,0.00324025,0.00357575,0.00393125,0.0043505,0.00230925,0.00261,0.002946,0.00332,0.003732,0.0042025,0.0047315,0.00532725,0.00598975,0.0067455,0.00147506,0.00155976,0.00174608,0.00198726,0.0022833,0.00263798,0.00305132,0.00352754,0.00406665,0.00500321,0.00650779,0.00846566,0.0102767
|
||||
26.421,54.3,0.788,0.784999998,0.978739,0.939434,0.902539,0.870067,0.84202,0.81526,0.789786,0.76862,0.751763,0.733198,1.28854,1.25794,1.22844,1.2021,1.17891,1.15711,1.13672,1.11838,1.1021,1.08347,1.18666,1.20227,1.22238,1.2451,1.27042,1.29448,1.31726,1.33969,1.36178,1.39131,1.42977,1.46072,1.47799,0.00165525,0.00177625,0.00190475,0.00205,0.002212,0.0023905,0.0025855,0.00281,0.003064,0.003337,0.0020525,0.002235,0.002431,0.00264825,0.00288675,0.00315125,0.00344175,0.0037635,0.0041165,0.004499,0.00157324,0.00165785,0.00184464,0.00208265,0.00237189,0.00270195,0.00307282,0.00349044,0.00395481,0.00475916,0.0060703,0.00768447,0.00913155
|
||||
29.717,32.7,0.708,0.634999908,1.04298,0.998871,0.956263,0.916092,0.878359,0.843323,0.810982,0.781669,0.755383,0.729026,1.37883,1.34638,1.31373,1.27917,1.24268,1.20612,1.16947,1.13735,1.10976,1.07884,1.18834,1.20439,1.22522,1.24777,1.27204,1.29707,1.32286,1.34873,1.37469,1.40614,1.43647,1.46244,1.48002,0.00173875,0.00185875,0.00198425,0.00212025,0.00226675,0.002427,0.002601,0.0027975,0.0030165,0.00324925,0.002167,0.002357,0.002557,0.00276725,0.00298775,0.00322075,0.00346625,0.00374125,0.00404575,0.00436325,0.00158059,0.00166113,0.00184234,0.0020718,0.00234949,0.00267342,0.0030436,0.00346154,0.00392725,0.00472746,0.0059784,0.00753397,0.00893714
|
||||
34.867,61.5,0.628,0.794999967,1.10063,1.07335,1.04852,1.0241,1.0001,0.980454,0.96517,0.947649,0.92789,0.912034,1.45198,1.45294,1.45591,1.45964,1.46413,1.46897,1.47417,1.47923,1.48414,1.48845,1.17741,1.19193,1.2115,1.23327,1.25723,1.28611,1.31991,1.35426,1.38916,1.44532,1.51315,1.58082,1.63227,0.0018785,0.0020575,0.0022565,0.002478,0.002722,0.0030065,0.0033315,0.0036845,0.0040655,0.0045165,0.00236975,0.00266375,0.00299525,0.00337425,0.00380075,0.0042865,0.0048315,0.00544925,0.00613975,0.0069265,0.00159775,0.00167529,0.00185345,0.00208477,0.00236923,0.00271669,0.00312715,0.00360434,0.00414823,0.00515747,0.00682307,0.00902998,0.0111152
|
||||
29.099,90.3,0.636,0.885000359,0.993097,0.962398,0.935508,0.91128,0.889714,0.869999,0.852136,0.839062,0.830774,0.820539,1.29573,1.28367,1.27576,1.268,1.2604,1.2556,1.25361,1.25428,1.25763,1.2637,1.17241,1.18458,1.20167,1.22349,1.25006,1.27739,1.30546,1.33445,1.36435,1.4045,1.45761,1.50447,1.54047,0.00174475,0.00189675,0.00206825,0.00226175,0.00247725,0.002723,0.002999,0.00332925,0.00371375,0.0041385,0.00216925,0.0024075,0.0026765,0.00297925,0.00331575,0.00370325,0.00414175,0.0046575,0.0052505,0.0059405,0.00165355,0.00172481,0.00190221,0.00213818,0.00243271,0.00277989,0.00317973,0.00364368,0.00417174,0.00510723,0.00667395,0.00870514,0.0106484
|
||||
33.219,108.3,0.772,0.724999978,0.956354,0.92135,0.890873,0.860411,0.829965,0.805773,0.787837,0.769456,0.750632,0.735227,1.2713,1.2443,1.2217,1.20059,1.18099,1.16168,1.14266,1.12438,1.10684,1.09205,1.1781,1.19934,1.22369,1.25142,1.28252,1.30871,1.32998,1.35103,1.37188,1.4099,1.44618,1.46815,1.48542,0.00161275,0.00172975,0.00185925,0.00199575,0.00213925,0.00230975,0.00250725,0.0027205,0.0029495,0.00321875,0.00201525,0.0021925,0.0023895,0.002605,0.002839,0.00309375,0.00336925,0.003674,0.004008,0.00438625,0.00159408,0.0016845,0.00187421,0.00211569,0.00240894,0.00273363,0.00308974,0.00349064,0.00393631,0.00474327,0.00597919,0.00747564,0.00883957
|
||||
27.245,48.9,0.956,0.744999875,0.975936,0.919364,0.866417,0.817485,0.77257,0.729435,0.68808,0.650834,0.617698,0.586692,1.29326,1.23653,1.17867,1.12317,1.07003,1.01645,0.96243,0.912074,0.865378,0.822492,1.20316,1.2205,1.24091,1.26113,1.28116,1.30286,1.32623,1.34565,1.36111,1.37995,1.39632,1.40117,1.40201,0.0015565,0.001634,0.001714,0.00179875,0.00188825,0.00197875,0.00207025,0.00217675,0.00229825,0.00242825,0.00192775,0.0020435,0.0021565,0.00227375,0.00239525,0.00251175,0.00262325,0.00274625,0.00288075,0.00302825,0.0015124,0.00160227,0.00178456,0.00200635,0.00226761,0.00256912,0.00291088,0.00328319,0.00368605,0.0043712,0.0054216,0.00667743,0.00779843
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35.897,65.1,0.74,0.775000018,1.07392,1.04393,1.01656,0.989526,0.962825,0.938231,0.915744,0.894589,0.874769,0.857622,1.43712,1.42915,1.42216,1.41728,1.41451,1.40929,1.40163,1.39089,1.37706,1.36816,1.18194,1.19963,1.22355,1.24959,1.27776,1.30753,1.33891,1.3696,1.3996,1.45146,1.51685,1.56482,1.59558,0.00181925,0.001983,0.002163,0.002356,0.002562,0.00279325,0.00304975,0.003333,0.003643,0.0040005,0.0023115,0.0025755,0.0028665,0.0031945,0.0035595,0.00395625,0.00438475,0.0048515,0.0053565,0.00594625,0.00156083,0.00164763,0.00183921,0.00208442,0.00238327,0.0027346,0.00313841,0.00359838,0.00411452,0.00505785,0.00659095,0.00847501,0.0101951
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35.279,74.1,0.724,0.655000038,1.04023,1.00123,0.964158,0.931051,0.901914,0.873386,0.845468,0.820295,0.797868,0.775029,1.39168,1.37099,1.34935,1.32595,1.3008,1.27668,1.25359,1.23115,1.20934,1.18524,1.18568,1.20479,1.22819,1.25246,1.27759,1.30638,1.33881,1.37014,1.40038,1.43363,1.47275,1.5086,1.52955,0.00175025,0.0018795,0.0020165,0.00216875,0.00233625,0.0025175,0.0027125,0.00292975,0.00316925,0.00343125,0.0022085,0.00242275,0.00265225,0.002895,0.003151,0.0034305,0.0037335,0.004068,0.004434,0.0048205,0.00158918,0.0016763,0.0018652,0.00210112,0.00238406,0.00272002,0.00310899,0.00354716,0.00403453,0.00484703,0.00614708,0.00776983,0.00922241
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34.249,104.7,0.812,1.00499978,1.01263,0.986166,0.963744,0.942858,0.92351,0.90828,0.897169,0.888313,0.881711,0.872734,1.34606,1.33997,1.33827,1.33821,1.33977,1.34444,1.35224,1.35806,1.36189,1.36573,1.17381,1.19206,1.21559,1.24217,1.27182,1.30106,1.32992,1.35927,1.38914,1.43551,1.49397,1.53679,1.56514,0.0017235,0.00188125,0.00205575,0.002249,0.002461,0.0027045,0.0029795,0.0032975,0.0036585,0.00404825,0.00217125,0.00242075,0.00270225,0.003018,0.003368,0.00377175,0.00422925,0.004737,0.005295,0.005925,0.00155669,0.00164344,0.00183563,0.00208209,0.00238283,0.0027318,0.003129,0.00358217,0.00409132,0.00500189,0.00647835,0.0082945,0.0099594
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28.687,113.7,0.836,0.924999856,0.918773,0.887821,0.862103,0.840172,0.822028,0.804911,0.788821,0.776059,0.766625,0.755581,1.20186,1.18047,1.16392,1.1498,1.1381,1.12859,1.12126,1.11707,1.11604,1.1128,1.17008,1.18949,1.2118,1.23611,1.26243,1.28665,1.30877,1.33007,1.35055,1.37678,1.41207,1.44775,1.47299,0.001546,0.0016675,0.0018045,0.0019595,0.0021325,0.0023235,0.0025325,0.0027755,0.0030525,0.00335575,0.00190325,0.00208225,0.00228275,0.00250775,0.00275725,0.003039,0.003353,0.003716,0.004128,0.00457775,0.00156896,0.00165452,0.00183913,0.00207312,0.0023565,0.00267725,0.00303538,0.003438,0.00388511,0.00465336,0.00589982,0.00748285,0.00893513
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27.451,99.3,0.804,0.805,0.910763,0.874111,0.842871,0.816473,0.794917,0.775205,0.757338,0.739644,0.722121,0.708471,1.19223,1.16354,1.13759,1.11537,1.09686,1.08011,1.0651,1.04897,1.03172,1.01802,1.17603,1.19609,1.21951,1.24158,1.2623,1.28507,1.30991,1.33161,1.35018,1.37324,1.40007,1.42366,1.43698,0.0015355,0.00164375,0.00176525,0.0019035,0.0020585,0.002233,0.002427,0.00264025,0.00287275,0.00314375,0.00188675,0.00204725,0.00222175,0.00241925,0.00263975,0.00288425,0.00315275,0.00344625,0.00376475,0.0041285,0.00158798,0.00167566,0.00186276,0.00209193,0.00236316,0.00267984,0.00304197,0.00344182,0.0038794,0.00462662,0.00581799,0.00731384,0.00864988
|
||||
33.837,36.3,0.868,0.764999889,1.0861,1.0482,1.01214,0.976858,0.942355,0.90838,0.874935,0.842208,0.8102,0.782814,1.45938,1.4372,1.41304,1.38729,1.35995,1.33158,1.30218,1.27095,1.23791,1.20365,1.19046,1.20829,1.23043,1.25619,1.28555,1.31499,1.34449,1.37175,1.39676,1.43227,1.47773,1.51904,1.53758,0.001768,0.00191075,0.00206225,0.002223,0.002393,0.0025715,0.0027585,0.00295875,0.00317225,0.00341825,0.00224375,0.00246925,0.00270975,0.002964,0.003232,0.003515,0.003813,0.004128,0.00446,0.004811,0.00149601,0.00158544,0.001771,0.00200992,0.00230218,0.00263958,0.00302212,0.00344957,0.00392195,0.00474526,0.0060635,0.00770556,0.00912415
|
||||
32.807,117.3,0.668,0.825000153,0.960685,0.929625,0.902121,0.878224,0.857937,0.839758,0.823686,0.809112,0.796033,0.785657,1.26558,1.2502,1.23793,1.22717,1.21792,1.21067,1.20542,1.2013,1.19832,1.19791,1.1721,1.18911,1.21244,1.23738,1.26394,1.29085,1.31811,1.34545,1.37287,1.40885,1.45288,1.49529,1.52485,0.00169025,0.001828,0.00198,0.00215525,0.00235375,0.00257825,0.00282875,0.003117,0.003443,0.0038185,0.002105,0.00232225,0.00256475,0.002837,0.003139,0.0034835,0.0038705,0.00431175,0.00480725,0.00538375,0.00166202,0.00174118,0.00193054,0.00217195,0.00246541,0.00280912,0.00320308,0.0036523,0.0041568,0.00504203,0.00648949,0.00836735,0.010109
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30.541,47.1,0.796,0.955000011,1.06639,1.03766,1.01262,0.988706,0.965923,0.94695,0.931787,0.917212,0.903225,0.888885,1.4125,1.40802,1.40725,1.40688,1.4069,1.40504,1.4013,1.40148,1.4056,1.41014,1.18002,1.19505,1.21616,1.23949,1.26503,1.29363,1.32528,1.35751,1.39032,1.44033,1.49406,1.54242,1.58681,0.001792,0.00196025,0.00214675,0.0023505,0.0025715,0.002825,0.003111,0.00343175,0.00378725,0.00417775,0.00227475,0.00254675,0.00285225,0.0031945,0.0035735,0.00398875,0.00444025,0.004962,0.005554,0.0062345,0.00153274,0.00161601,0.00180334,0.00204427,0.00233881,0.00268922,0.00309549,0.00356242,0.00409001,0.00503577,0.00653008,0.00843803,0.0102645
|
||||
32.189,84.9,0.932,0.835000291,0.995785,0.958093,0.924415,0.893143,0.864275,0.838154,0.814777,0.79166,0.768803,0.746787,1.32468,1.29902,1.27789,1.25562,1.23222,1.20951,1.18749,1.16446,1.14043,1.11673,1.18852,1.20822,1.23244,1.25702,1.28196,1.30663,1.33105,1.35654,1.3831,1.41628,1.45056,1.47742,1.49563,0.00163075,0.001752,0.001884,0.002028,0.002184,0.00235525,0.00254175,0.0027455,0.0029665,0.00320675,0.00204125,0.00223,0.002438,0.002661,0.002899,0.00315475,0.00342825,0.00372425,0.00404275,0.00439,0.00151777,0.00161049,0.00180176,0.00203854,0.00232086,0.00264304,0.0030051,0.00341571,0.00387485,0.00466104,0.00588025,0.00737695,0.0087308
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32.601,34.5,0.948,0.965000009,1.09462,1.06375,1.03432,1.00573,0.977984,0.952835,0.930285,0.908338,0.886993,0.867714,1.46065,1.45306,1.44753,1.43947,1.42887,1.4197,1.41194,1.40257,1.39158,1.37972,1.18784,1.20364,1.22371,1.24792,1.27627,1.30532,1.33505,1.36668,1.40022,1.44678,1.50436,1.55693,1.59031,0.001779,0.00194275,0.00211825,0.002307,0.002509,0.00273475,0.00298425,0.003257,0.003553,0.0038865,0.0022535,0.002518,0.002812,0.00312775,0.00346525,0.00384125,0.00425575,0.0047115,0.0052085,0.005751,0.00146867,0.00155384,0.00173641,0.00197448,0.00226806,0.00261054,0.00300192,0.00345359,0.00396553,0.00485784,0.00629849,0.00811763,0.00975208
|
||||
33.425,106.5,0.892,0.684999897,0.976449,0.93286,0.892331,0.855684,0.822918,0.792691,0.765001,0.739283,0.715539,0.691747,1.29326,1.25859,1.22585,1.1914,1.15523,1.12247,1.09311,1.06187,1.02875,0.996137,1.18384,1.20432,1.22706,1.25091,1.27588,1.30069,1.32534,1.34997,1.3746,1.39842,1.42281,1.43707,1.44012,0.00154,0.001627,0.001719,0.0018205,0.0019315,0.0020525,0.0021835,0.00232825,0.00248675,0.00265425,0.00190825,0.002047,0.002195,0.0023465,0.0025015,0.00267175,0.00285725,0.0030465,0.0032395,0.00344575,0.00152873,0.00161739,0.00179422,0.00201024,0.00226544,0.00255341,0.00287415,0.00323274,0.00362919,0.00427432,0.00527185,0.00644644,0.00746019
|
||||
25.803,45.3,0.82,0.66500009,0.96154,0.90709,0.855365,0.809034,0.768097,0.729528,0.693325,0.659993,0.62953,0.600347,1.26265,1.20932,1.15833,1.10654,1.05396,1.00435,0.957731,0.914714,0.875304,0.837836,1.19915,1.21598,1.2357,1.25551,1.27542,1.29492,1.31401,1.33409,1.35514,1.37012,1.37922,1.38834,1.3958,0.00156225,0.00163925,0.00171775,0.00180625,0.00190475,0.0020075,0.0021145,0.00223325,0.00236375,0.002506,0.0019185,0.00203675,0.00216025,0.002281,0.002399,0.00252425,0.00265675,0.00280175,0.00295925,0.00313075,0.00156087,0.00164604,0.00182556,0.00204639,0.00230852,0.00260565,0.00293777,0.00331349,0.00373282,0.00440617,0.00543518,0.00670119,0.0078516
|
||||
29.923,77.7,0.94,0.625000083,1.01788,0.95503,0.894595,0.838108,0.78557,0.735579,0.688137,0.644446,0.604505,0.567963,1.33811,1.26851,1.19497,1.12898,1.07054,1.00563,0.93426,0.870479,0.814287,0.764954,1.20309,1.22108,1.23856,1.25735,1.27745,1.29683,1.3155,1.3286,1.33615,1.35567,1.36192,1.34871,1.34701,0.001804,0.0018585,0.0019135,0.00196975,0.00202725,0.00208475,0.00214225,0.002206,0.002276,0.00235325,0.006264,0.00648925,0.00668175,0.00689975,0.00714325,0.0073285,0.0074555,0.00760325,0.00777175,0.00799175,0.00385201,0.00403879,0.00441147,0.00485582,0.00537186,0.00595744,0.00661257,0.00730483,0.00803423,0.00933115,0.0112061,0.0133012,0.0152031
|
||||
26.833,75.9,0.756,1.025000455,0.99508,0.966113,0.94071,0.918474,0.899405,0.8834,0.870459,0.856826,0.842501,0.831109,1.29794,1.28535,1.27528,1.2698,1.2689,1.26929,1.27096,1.27741,1.28867,1.29743,1.174,1.18734,1.20462,1.22622,1.25213,1.27839,1.30501,1.33093,1.35617,1.39711,1.44872,1.51071,1.56127,0.0017075,0.00185925,0.00202775,0.002218,0.00243,0.0026735,0.0029485,0.003252,0.003584,0.00397025,0.0021165,0.002347,0.002603,0.0029,0.003238,0.0036215,0.0040505,0.004552,0.005126,0.00577825,0.00158331,0.00165991,0.00184034,0.00207689,0.00236958,0.0027106,0.00309996,0.00354233,0.0040377,0.00493378,0.00639704,0.00835915,0.0102139
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<!-- navigation toc: --> <li><a href="._Intro2Course-bs001.html#___sec0" style="font-size: 80%;">Overview of first week</a></li>
|
||||
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|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs003.html#___sec2" style="font-size: 80%;">Course Format</a></li>
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|
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<!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers</a></li>
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<!-- navigation toc: --> <li><a href="._Intro2Course-bs005.html#___sec4" style="font-size: 80%;">Deadlines for projects (tentative)</a></li>
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<!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Learning outcomes</a></li>
|
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<!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Prerequisites</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Learning outcomes</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs011.html#___sec10" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -129,7 +131,7 @@ end of tocinfo -->
|
||||
<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
|
||||
<br>
|
||||
<p>
|
||||
<center><h4>Nov 12, 2019</h4></center> <!-- date -->
|
||||
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||||
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|
||||
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|
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||||
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<li><a href="._Intro2Course-bs008.html">9</a></li>
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||||
<li><a href="._Intro2Course-bs009.html">10</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._Intro2Course-bs010.html">11</a></li>
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||||
<li><a href="._Intro2Course-bs011.html">12</a></li>
|
||||
<li><a href="._Intro2Course-bs001.html">»</a></li>
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<center style="font-size:80%">
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<!-- copyright --> © 1999-2019, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
|
||||
<!-- copyright --> © 1999-2020, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
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</center>
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||||
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||||
None,
|
||||
'___sec9')]}
|
||||
'___sec10')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -88,13 +89,14 @@ end of tocinfo -->
|
||||
<!-- navigation toc: --> <li><a href="#___sec0" style="font-size: 80%;">Overview of first week</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs002.html#___sec1" style="font-size: 80%;">Lectures and ComputerLab</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs003.html#___sec2" style="font-size: 80%;">Course Format</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers and ComputerLab</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs005.html#___sec4" style="font-size: 80%;">Deadlines for projects (tentative)</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Learning outcomes</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Prerequisites</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Learning outcomes</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs011.html#___sec10" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -118,10 +120,10 @@ end of tocinfo -->
|
||||
<p> <!-- subsequent paragraphs come in larger fonts, so start with a paragraph -->
|
||||
|
||||
<ul>
|
||||
<li> Thursday August 22: First lecture: Presentation of the course, aims and content</li>
|
||||
<li> Thursday: Second Lecture: Start with simple linear regression and repetition of linear algebra</li>
|
||||
<li> Friday August 23: Linear regression</li>
|
||||
<li> Computer lab: Tuesday. First time: Tuesday August 27.</li>
|
||||
<li> Thursday August 20: First lecture: Presentation of the course, aims and content</li>
|
||||
<li> Thursday: Second Lecture: Start with simple linear regression and repetition of linear algebra and elements of statistics</li>
|
||||
<li> Friday August 21: Linear regression</li>
|
||||
<li> Computer lab: Wednesdays, 8am-6pm. First time: Wednesday August 26.</li>
|
||||
</ul>
|
||||
</div>
|
||||
</div>
|
||||
@@ -143,6 +145,8 @@ end of tocinfo -->
|
||||
<li><a href="._Intro2Course-bs008.html">9</a></li>
|
||||
<li><a href="._Intro2Course-bs009.html">10</a></li>
|
||||
<li><a href="._Intro2Course-bs010.html">11</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._Intro2Course-bs011.html">12</a></li>
|
||||
<li><a href="._Intro2Course-bs002.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -44,26 +44,27 @@ Automatically generated HTML file from DocOnce source
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
('Prerequisites', 2, None, '___sec5'),
|
||||
('Learning outcomes', 2, None, '___sec6'),
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
'___sec8'),
|
||||
('Extremely useful tools, strongly recommended',
|
||||
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|
||||
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|
||||
'___sec8'),
|
||||
'___sec9'),
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
||||
<body>
|
||||
@@ -88,13 +89,14 @@ end of tocinfo -->
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs001.html#___sec0" style="font-size: 80%;">Overview of first week</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec1" style="font-size: 80%;">Lectures and ComputerLab</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs003.html#___sec2" style="font-size: 80%;">Course Format</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers and ComputerLab</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs005.html#___sec4" style="font-size: 80%;">Deadlines for projects (tentative)</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Learning outcomes</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Prerequisites</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Learning outcomes</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs011.html#___sec10" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -118,12 +120,10 @@ end of tocinfo -->
|
||||
<p> <!-- subsequent paragraphs come in larger fonts, so start with a paragraph -->
|
||||
|
||||
<ul>
|
||||
<li> Lectures: Thursday (2.15pm-4pm, this may change) and Friday (12.15pm-2pm).</li>
|
||||
<li> Weekly reading assignments needed to solve projects and exercises.</li>
|
||||
<li> Lectures: Thursday (12.15pm-2pm and Friday (12.15pm-2pm). Due to the present COVID-19 situation all lectures will be online. They will be recorded and posted online at the official UiO <a href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/index.html" target="_self">website</a>.</li>
|
||||
<li> Weekly reading assignments and videos needed to solve projects and exercises.</li>
|
||||
<li> Weekly exercises when not working on projects. You can hand in exercises if you want.</li>
|
||||
<li> First hour of each lab session may be used to discuss technicalities, address questions etc linked with projects and exercises.</li>
|
||||
<li> Detailed lecture notes, exercises, all programs presented, projects etc can be found at the homepage of the course.</li>
|
||||
<li> Computerlab: Tuesday (8am-4pm), VB IT-auditorium 3. Depending on how many enlist we may extend the lab sessions</li>
|
||||
<li> Weekly plans and all other information are on the official webpage.</li>
|
||||
<li> No final exam, three projects that are graded and have to be approved.</li>
|
||||
</ul>
|
||||
@@ -147,6 +147,7 @@ end of tocinfo -->
|
||||
<li><a href="._Intro2Course-bs008.html">9</a></li>
|
||||
<li><a href="._Intro2Course-bs009.html">10</a></li>
|
||||
<li><a href="._Intro2Course-bs010.html">11</a></li>
|
||||
<li><a href="._Intro2Course-bs011.html">12</a></li>
|
||||
<li><a href="._Intro2Course-bs003.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -44,26 +44,27 @@ Automatically generated HTML file from DocOnce source
|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
'___sec6'),
|
||||
'___sec7'),
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
'___sec8'),
|
||||
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|
||||
2,
|
||||
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|
||||
'___sec8'),
|
||||
'___sec9'),
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -88,13 +89,14 @@ end of tocinfo -->
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs001.html#___sec0" style="font-size: 80%;">Overview of first week</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs002.html#___sec1" style="font-size: 80%;">Lectures and ComputerLab</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec2" style="font-size: 80%;">Course Format</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers and ComputerLab</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs005.html#___sec4" style="font-size: 80%;">Deadlines for projects (tentative)</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Learning outcomes</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Prerequisites</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Learning outcomes</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs011.html#___sec10" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -150,6 +152,7 @@ end of tocinfo -->
|
||||
<li><a href="._Intro2Course-bs008.html">9</a></li>
|
||||
<li><a href="._Intro2Course-bs009.html">10</a></li>
|
||||
<li><a href="._Intro2Course-bs010.html">11</a></li>
|
||||
<li><a href="._Intro2Course-bs011.html">12</a></li>
|
||||
<li><a href="._Intro2Course-bs004.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -44,26 +44,27 @@ Automatically generated HTML file from DocOnce source
|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
('Learning outcomes', 2, None, '___sec6'),
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
'___sec8'),
|
||||
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|
||||
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|
||||
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|
||||
'___sec8'),
|
||||
'___sec9'),
|
||||
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||||
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||||
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||||
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||||
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|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -88,13 +89,14 @@ end of tocinfo -->
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs001.html#___sec0" style="font-size: 80%;">Overview of first week</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs002.html#___sec1" style="font-size: 80%;">Lectures and ComputerLab</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs003.html#___sec2" style="font-size: 80%;">Course Format</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec3" style="font-size: 80%;">Teachers and ComputerLab</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec3" style="font-size: 80%;">Teachers</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs005.html#___sec4" style="font-size: 80%;">Deadlines for projects (tentative)</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Learning outcomes</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Prerequisites</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Learning outcomes</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs011.html#___sec10" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -110,7 +112,7 @@ end of tocinfo -->
|
||||
<a name="part0004"></a>
|
||||
<!-- !split -->
|
||||
|
||||
<h2 id="___sec3" class="anchor">Teachers and ComputerLab </h2>
|
||||
<h2 id="___sec3" class="anchor">Teachers </h2>
|
||||
|
||||
<p>
|
||||
<div class="panel panel-default">
|
||||
@@ -120,31 +122,26 @@ end of tocinfo -->
|
||||
<p>
|
||||
<b>Teachers :</b>
|
||||
|
||||
<ol>
|
||||
<li> <a href="https://www.researchgate.net/profile/Hanna_Svennevik" target="_self">Hanna Svennevik</a></li>
|
||||
<li> <a href="http://mhjgit.github.io/info/doc/web/" target="_self">Morten Hjorth-Jensen</a></li>
|
||||
<li> <a href="https://no.linkedin.com/in/lucas-charpentier-176206171" target="_self">Lucas Charpentier</a></li>
|
||||
<li> <a href="https://www.researchgate.net/profile/Stian_Bilek" target="_self">Stian Bilek</a></li>
|
||||
<li> <a href="https://github.com/Schoyen" target="_self">Øyvind Sigmundson Schøyen</a></li>
|
||||
</ol>
|
||||
<ul>
|
||||
<li> Morten Hjorth-Jensen, morten.hjorth-jensen@fys.uio.no</li>
|
||||
|
||||
<ul>
|
||||
<li> <b>Phone</b>: +47-48257387</li>
|
||||
<li> <b>Office</b>: Department of Physics, University of Oslo, Eastern wing, room FØ470</li>
|
||||
<li> <b>Office hours</b>: <em>Anytime</em>! In Fall Semester 2020 (FS20), as a rule of thumb office hours are planned via computer or telephone. Individual or group office hours will be performed via zoom. Feel free to send an email for planning. In person meetings may also be possible if allowed by the University of Oslo's COVID-19 instructions.</li>
|
||||
</ul>
|
||||
|
||||
<div class="row">
|
||||
<div class="col-xs-3">
|
||||
<table class="table table-striped table-hover table-condensed">
|
||||
<thead>
|
||||
<tr><td align="center"><b> day </b></td> <td align="center"><b> Time </b></td> </tr>
|
||||
</thead>
|
||||
<tbody>
|
||||
<tr><td align="center"> Group 1: Tuesday </td> <td align="center"> 8am-10am </td> </tr>
|
||||
<tr><td align="center"> Group 2: Tuesday </td> <td align="center"> 10am-12pm </td> </tr>
|
||||
<tr><td align="center"> Group 3: Tuesday </td> <td align="center"> 12pm-2pm </td> </tr>
|
||||
<tr><td align="center"> Group 4: Tuesday </td> <td align="center"> 2pm-4pm </td> </tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</div> <!-- col-xs-3 -->
|
||||
</div> <!-- cell row -->
|
||||
<p>
|
||||
<li> Øyvind Sigmundson Schøyen, oyvinssc@student.matnat.uio.no</li>
|
||||
|
||||
<ul>
|
||||
<li> <b>Office</b>: Department of Physics, University of Oslo, Eastern wing, room FØ452</li>
|
||||
</ul>
|
||||
|
||||
<li> Michael Bitney, m.s.bitney@fys.uio.no</li>
|
||||
<li> Kristian Wold, kriswold@student.matnat.uio.no</li>
|
||||
<li> Nicolai Haug, nicoha@student.matnat.uio.no</li>
|
||||
<li> Per-Dimitri Sønsteland, perdimitri.bs@gmail.com</li>
|
||||
</ul>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
@@ -165,6 +162,7 @@ end of tocinfo -->
|
||||
<li><a href="._Intro2Course-bs008.html">9</a></li>
|
||||
<li><a href="._Intro2Course-bs009.html">10</a></li>
|
||||
<li><a href="._Intro2Course-bs010.html">11</a></li>
|
||||
<li><a href="._Intro2Course-bs011.html">12</a></li>
|
||||
<li><a href="._Intro2Course-bs005.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -44,26 +44,27 @@ Automatically generated HTML file from DocOnce source
|
||||
'sections': [('Overview of first week', 2, None, '___sec0'),
|
||||
('Lectures and ComputerLab', 2, None, '___sec1'),
|
||||
('Course Format', 2, None, '___sec2'),
|
||||
('Teachers and ComputerLab', 2, None, '___sec3'),
|
||||
('Teachers', 2, None, '___sec3'),
|
||||
('Deadlines for projects (tentative)', 2, None, '___sec4'),
|
||||
('Learning outcomes', 2, None, '___sec5'),
|
||||
('Prerequisites', 2, None, '___sec5'),
|
||||
('Learning outcomes', 2, None, '___sec6'),
|
||||
('Topics covered in this course: Statistical analysis and '
|
||||
'optimization of data',
|
||||
2,
|
||||
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|
||||
'___sec6'),
|
||||
'___sec7'),
|
||||
('Topics covered in this course: Machine Learning',
|
||||
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|
||||
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|
||||
'___sec7'),
|
||||
'___sec8'),
|
||||
('Extremely useful tools, strongly recommended',
|
||||
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|
||||
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|
||||
'___sec8'),
|
||||
'___sec9'),
|
||||
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|
||||
2,
|
||||
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|
||||
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|
||||
'___sec10')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -88,13 +89,14 @@ end of tocinfo -->
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs001.html#___sec0" style="font-size: 80%;">Overview of first week</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs002.html#___sec1" style="font-size: 80%;">Lectures and ComputerLab</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs003.html#___sec2" style="font-size: 80%;">Course Format</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers and ComputerLab</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec4" style="font-size: 80%;">Deadlines for projects (tentative)</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Learning outcomes</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Prerequisites</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Learning outcomes</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs011.html#___sec10" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -118,12 +120,12 @@ end of tocinfo -->
|
||||
<p> <!-- subsequent paragraphs come in larger fonts, so start with a paragraph -->
|
||||
|
||||
<ol>
|
||||
<li> Project 1: September 30 (graded with feedback)</li>
|
||||
<li> Project 2: November 13 (graded with feedback)</li>
|
||||
<li> Project 3: December 15 (graded with feedback)</li>
|
||||
<li> Project 1: September 28 (graded with feedback)</li>
|
||||
<li> Project 2: November 2 (graded with feedback)</li>
|
||||
<li> Project 3: December 7 (graded with feedback)</li>
|
||||
</ol>
|
||||
|
||||
Projects are handed in using devilry.ifi.uio.no. We use Github as repository for codes, benchmark calculations etc. Comments and feedback on projects only via devilry.
|
||||
Projects are handed in using <b>Canvas</b>. We use Github as repository for codes, benchmark calculations etc. Comments and feedback on projects only via <b>Canvas</b>.
|
||||
|
||||
<p>
|
||||
</div>
|
||||
@@ -146,6 +148,7 @@ Projects are handed in using devilry.ifi.uio.no. We use Github as repository for
|
||||
<li><a href="._Intro2Course-bs008.html">9</a></li>
|
||||
<li><a href="._Intro2Course-bs009.html">10</a></li>
|
||||
<li><a href="._Intro2Course-bs010.html">11</a></li>
|
||||
<li><a href="._Intro2Course-bs011.html">12</a></li>
|
||||
<li><a href="._Intro2Course-bs006.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -44,26 +44,27 @@ Automatically generated HTML file from DocOnce source
|
||||
'sections': [('Overview of first week', 2, None, '___sec0'),
|
||||
('Lectures and ComputerLab', 2, None, '___sec1'),
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
('Prerequisites', 2, None, '___sec5'),
|
||||
('Learning outcomes', 2, None, '___sec6'),
|
||||
('Topics covered in this course: Statistical analysis and '
|
||||
'optimization of data',
|
||||
2,
|
||||
None,
|
||||
'___sec6'),
|
||||
'___sec7'),
|
||||
('Topics covered in this course: Machine Learning',
|
||||
2,
|
||||
None,
|
||||
'___sec7'),
|
||||
'___sec8'),
|
||||
('Extremely useful tools, strongly recommended',
|
||||
2,
|
||||
None,
|
||||
'___sec8'),
|
||||
'___sec9'),
|
||||
('Other courses on Data science and Machine Learning at UiO',
|
||||
2,
|
||||
None,
|
||||
'___sec9')]}
|
||||
'___sec10')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -88,13 +89,14 @@ end of tocinfo -->
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs001.html#___sec0" style="font-size: 80%;">Overview of first week</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs002.html#___sec1" style="font-size: 80%;">Lectures and ComputerLab</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs003.html#___sec2" style="font-size: 80%;">Course Format</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers and ComputerLab</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs005.html#___sec4" style="font-size: 80%;">Deadlines for projects (tentative)</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec5" style="font-size: 80%;">Learning outcomes</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec5" style="font-size: 80%;">Prerequisites</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Learning outcomes</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs011.html#___sec10" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -110,28 +112,18 @@ end of tocinfo -->
|
||||
<a name="part0006"></a>
|
||||
<!-- !split -->
|
||||
|
||||
<h2 id="___sec5" class="anchor">Learning outcomes </h2>
|
||||
<h2 id="___sec5" class="anchor">Prerequisites </h2>
|
||||
|
||||
<p>
|
||||
<div class="panel panel-default">
|
||||
<div class="panel-body">
|
||||
<p> <!-- subsequent paragraphs come in larger fonts, so start with a paragraph -->
|
||||
|
||||
<ul>
|
||||
<li> Learn about basic data analysis, statistical analysis, Bayesian statistics, Monte Carlo sampling, data optimization and machine learning</li>
|
||||
<li> Be capable of extending the acquired knowledge to other systems and cases</li>
|
||||
<li> Have an understanding of central algorithms used in data analysis and machine learning</li>
|
||||
<li> Gain knowledge of central aspects of Monte Carlo methods, Markov chains, Gibbs samplers and their possible applications</li>
|
||||
<li> Understand linear methods for regression and classification, from ordinary least squares, via Lasso and Ridge to Logistic regression</li>
|
||||
<li> Learn about various neural networks and deep learning methods for supervised and unsupervised learning</li>
|
||||
<li> Learn about about decision trees and random forests</li>
|
||||
<li> Learn about support vector machines and kernel transformations</li>
|
||||
<li> Reduction of data sets, from PCA to clustering, supervised and unsupervided methods</li>
|
||||
<li> Work on numerical projects to illustrate the theory. The projects play a central role and you are expected to know modern programming languages like Python or C++</li>
|
||||
</ul>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
Basic knowledge in programming and mathematics, with an emphasis on
|
||||
linear algebra. Knowledge of Python or/and C++ as programming
|
||||
languages is strongly recommended and experience with Jupiter notebook
|
||||
is recommended. Required courses are the equivalents to the University
|
||||
of Oslo mathematics courses MAT1100, MAT1110, MAT1120 and at least one
|
||||
of the corresponding computing and programming courses INF1000/INF1110
|
||||
or MAT-INF1100/MAT-INF1100L/BIOS1100/KJM-INF1100. Most universities
|
||||
offer nowadays a basic programming course (often compulsory) where
|
||||
Python is the recurring programming language.
|
||||
|
||||
<p>
|
||||
<p>
|
||||
@@ -149,6 +141,7 @@ end of tocinfo -->
|
||||
<li><a href="._Intro2Course-bs008.html">9</a></li>
|
||||
<li><a href="._Intro2Course-bs009.html">10</a></li>
|
||||
<li><a href="._Intro2Course-bs010.html">11</a></li>
|
||||
<li><a href="._Intro2Course-bs011.html">12</a></li>
|
||||
<li><a href="._Intro2Course-bs007.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -44,26 +44,27 @@ Automatically generated HTML file from DocOnce source
|
||||
'sections': [('Overview of first week', 2, None, '___sec0'),
|
||||
('Lectures and ComputerLab', 2, None, '___sec1'),
|
||||
('Course Format', 2, None, '___sec2'),
|
||||
('Teachers and ComputerLab', 2, None, '___sec3'),
|
||||
('Teachers', 2, None, '___sec3'),
|
||||
('Deadlines for projects (tentative)', 2, None, '___sec4'),
|
||||
('Learning outcomes', 2, None, '___sec5'),
|
||||
('Prerequisites', 2, None, '___sec5'),
|
||||
('Learning outcomes', 2, None, '___sec6'),
|
||||
('Topics covered in this course: Statistical analysis and '
|
||||
'optimization of data',
|
||||
2,
|
||||
None,
|
||||
'___sec6'),
|
||||
'___sec7'),
|
||||
('Topics covered in this course: Machine Learning',
|
||||
2,
|
||||
None,
|
||||
'___sec7'),
|
||||
'___sec8'),
|
||||
('Extremely useful tools, strongly recommended',
|
||||
2,
|
||||
None,
|
||||
'___sec8'),
|
||||
'___sec9'),
|
||||
('Other courses on Data science and Machine Learning at UiO',
|
||||
2,
|
||||
None,
|
||||
'___sec9')]}
|
||||
'___sec10')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -88,13 +89,14 @@ end of tocinfo -->
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs001.html#___sec0" style="font-size: 80%;">Overview of first week</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs002.html#___sec1" style="font-size: 80%;">Lectures and ComputerLab</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs003.html#___sec2" style="font-size: 80%;">Course Format</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers and ComputerLab</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs005.html#___sec4" style="font-size: 80%;">Deadlines for projects (tentative)</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Learning outcomes</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec6" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Prerequisites</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec6" style="font-size: 80%;">Learning outcomes</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs011.html#___sec10" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -110,22 +112,27 @@ end of tocinfo -->
|
||||
<a name="part0007"></a>
|
||||
<!-- !split -->
|
||||
|
||||
<h2 id="___sec6" class="anchor">Topics covered in this course: Statistical analysis and optimization of data </h2>
|
||||
<h2 id="___sec6" class="anchor">Learning outcomes </h2>
|
||||
|
||||
<p>
|
||||
<div class="panel panel-default">
|
||||
<div class="panel-body">
|
||||
<p> <!-- subsequent paragraphs come in larger fonts, so start with a paragraph -->
|
||||
|
||||
<p>
|
||||
This course aims at giving you insights and knowledge about many of the central algorithms used in Data Analysis and Machine Learning. The course is project based and through various numerical projects, normally three, you will be exposed to fundamental research problems in these fields, with the aim to reproduce state of the art scientific results. Both supervised and unsupervised methods will be covered. The emphasis is on a frequentist approach, although we will try to link it with a Bayesian approach as well. You will learn to develop and structure large codes for studying different cases where Machine Learning is applied to, get acquainted with computing facilities and learn to handle large scientific projects. A good scientific and ethical conduct is emphasized throughout the course. More specifically, after this course you will
|
||||
|
||||
<ul>
|
||||
<li> Basic concepts, expectation values, variance, covariance, correlation functions and errors</li>
|
||||
<li> Simpler models, binomial distribution, the Poisson distribution, simple and multivariate normal distributions</li>
|
||||
<li> Central elements of Bayesian statistics and modeling</li>
|
||||
<li> Gradient methods for data optimization</li>
|
||||
<li> Monte Carlo methods, Markov chains, Metropolis-Hastings algorithm</li>
|
||||
<li> Linear methods for regression and classification</li>
|
||||
<li> Estimation of errors using cross-validation, blocking, bootstrapping and jackknife methods</li>
|
||||
<li> Practical optimization using Singular-value decomposition and least squares for parameterizing data</li>
|
||||
<li> Learn about basic data analysis, statistical analysis, Bayesian statistics, Monte Carlo sampling, data optimization and machine learning;</li>
|
||||
<li> Be capable of extending the acquired knowledge to other systems and cases;</li>
|
||||
<li> Have an understanding of central algorithms used in data analysis and machine learning;</li>
|
||||
<li> Understand linear methods for regression and classification, from ordinary least squares, via Lasso and Ridge to Logistic regression;</li>
|
||||
<li> Learn about neural networks and deep learning methods for supervised and unsupervised learning. Emphasis on feed forward neural networks, convolutional and recurrent neural networks;</li>
|
||||
<li> Learn about about decision trees, random forests, bagging and boosting methods;</li>
|
||||
<li> Learn about support vector machines and kernel transformations;</li>
|
||||
<li> Reduction of data sets, from PCA to clustering;</li>
|
||||
<li> Autoencoders and Reinforcement Learning;</li>
|
||||
<li> Work on numerical projects to illustrate the theory. The projects play a central role and you are expected to know modern programming languages like Python or C++ and/or Fortran (Fortran2003 or later).</li>
|
||||
</ul>
|
||||
</div>
|
||||
</div>
|
||||
@@ -147,6 +154,7 @@ end of tocinfo -->
|
||||
<li><a href="._Intro2Course-bs008.html">9</a></li>
|
||||
<li><a href="._Intro2Course-bs009.html">10</a></li>
|
||||
<li><a href="._Intro2Course-bs010.html">11</a></li>
|
||||
<li><a href="._Intro2Course-bs011.html">12</a></li>
|
||||
<li><a href="._Intro2Course-bs008.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -44,26 +44,27 @@ Automatically generated HTML file from DocOnce source
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
('Prerequisites', 2, None, '___sec5'),
|
||||
('Learning outcomes', 2, None, '___sec6'),
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
'___sec7'),
|
||||
'___sec8'),
|
||||
('Extremely useful tools, strongly recommended',
|
||||
2,
|
||||
None,
|
||||
'___sec8'),
|
||||
'___sec9'),
|
||||
('Other courses on Data science and Machine Learning at UiO',
|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs002.html#___sec1" style="font-size: 80%;">Lectures and ComputerLab</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs003.html#___sec2" style="font-size: 80%;">Course Format</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers and ComputerLab</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs005.html#___sec4" style="font-size: 80%;">Deadlines for projects (tentative)</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Learning outcomes</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec7" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Prerequisites</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Learning outcomes</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec7" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs011.html#___sec10" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -110,22 +112,34 @@ end of tocinfo -->
|
||||
<a name="part0008"></a>
|
||||
<!-- !split -->
|
||||
|
||||
<h2 id="___sec7" class="anchor">Topics covered in this course: Machine Learning </h2>
|
||||
<h2 id="___sec7" class="anchor">Topics covered in this course: Statistical analysis and optimization of data </h2>
|
||||
|
||||
<p>
|
||||
The course has two central parts
|
||||
|
||||
<ol>
|
||||
<li> Statistical analysis and optimization of data</li>
|
||||
<li> Machine learning</li>
|
||||
</ol>
|
||||
|
||||
These topics will be scattered thorughout the course and may not necessarily be taught separately. Rather, we will often take an approach (during the lectures and project/exercise sessions) where say elements from statistical data analysis are mixed with specific Machine Learning algorithms
|
||||
|
||||
<p>
|
||||
<div class="panel panel-default">
|
||||
<div class="panel-body">
|
||||
<p> <!-- subsequent paragraphs come in larger fonts, so start with a paragraph -->
|
||||
|
||||
<p>
|
||||
The following topics will be covered
|
||||
|
||||
<ul>
|
||||
<li> Linear Regression and Logistic Regression</li>
|
||||
<li> Neural networks and deep learning</li>
|
||||
<li> Decisions trees and nearest neighbor algorithms</li>
|
||||
<li> Support vector machines</li>
|
||||
<li> Bayesian Neural Networks</li>
|
||||
<li> Boltzmann Machines</li>
|
||||
<li> Dimensionality reduction, from PCA to cluster models</li>
|
||||
<li> Basic concepts, expectation values, variance, covariance, correlation functions and errors;</li>
|
||||
<li> Simpler models, binomial distribution, the Poisson distribution, simple and multivariate normal distributions;</li>
|
||||
<li> Central elements of Bayesian statistics and modeling;</li>
|
||||
<li> Gradient methods for data optimization,</li>
|
||||
<li> Monte Carlo methods, Markov chains, Gibbs sampling and Metropolis-Hastings sampling;</li>
|
||||
<li> Estimation of errors and resampling techniques such as the cross-validation, blocking, bootstrapping and jackknife methods;</li>
|
||||
<li> Principal Component Analysis (PCA) and its mathematical foundation</li>
|
||||
</ul>
|
||||
</div>
|
||||
</div>
|
||||
@@ -147,6 +161,7 @@ The following topics will be covered
|
||||
<li class="active"><a href="._Intro2Course-bs008.html">9</a></li>
|
||||
<li><a href="._Intro2Course-bs009.html">10</a></li>
|
||||
<li><a href="._Intro2Course-bs010.html">11</a></li>
|
||||
<li><a href="._Intro2Course-bs011.html">12</a></li>
|
||||
<li><a href="._Intro2Course-bs009.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -44,26 +44,27 @@ Automatically generated HTML file from DocOnce source
|
||||
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|
||||
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|
||||
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|
||||
('Teachers and ComputerLab', 2, None, '___sec3'),
|
||||
('Teachers', 2, None, '___sec3'),
|
||||
('Deadlines for projects (tentative)', 2, None, '___sec4'),
|
||||
('Learning outcomes', 2, None, '___sec5'),
|
||||
('Prerequisites', 2, None, '___sec5'),
|
||||
('Learning outcomes', 2, None, '___sec6'),
|
||||
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|
||||
'optimization of data',
|
||||
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|
||||
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|
||||
'___sec6'),
|
||||
'___sec7'),
|
||||
('Topics covered in this course: Machine Learning',
|
||||
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|
||||
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|
||||
'___sec7'),
|
||||
'___sec8'),
|
||||
('Extremely useful tools, strongly recommended',
|
||||
2,
|
||||
None,
|
||||
'___sec8'),
|
||||
'___sec9'),
|
||||
('Other courses on Data science and Machine Learning at UiO',
|
||||
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|
||||
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|
||||
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|
||||
'___sec10')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -88,13 +89,14 @@ end of tocinfo -->
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs001.html#___sec0" style="font-size: 80%;">Overview of first week</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs002.html#___sec1" style="font-size: 80%;">Lectures and ComputerLab</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs003.html#___sec2" style="font-size: 80%;">Course Format</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers and ComputerLab</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs005.html#___sec4" style="font-size: 80%;">Deadlines for projects (tentative)</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Learning outcomes</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec8" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Prerequisites</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Learning outcomes</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec8" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs011.html#___sec10" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -110,18 +112,27 @@ end of tocinfo -->
|
||||
<a name="part0009"></a>
|
||||
<!-- !split -->
|
||||
|
||||
<h2 id="___sec8" class="anchor">Extremely useful tools, strongly recommended </h2>
|
||||
<h2 id="___sec8" class="anchor">Topics covered in this course: Machine Learning </h2>
|
||||
|
||||
<p>
|
||||
<div class="panel panel-default">
|
||||
<div class="panel-body">
|
||||
<p> <!-- subsequent paragraphs come in larger fonts, so start with a paragraph -->
|
||||
The following topics will be covered
|
||||
|
||||
<ul>
|
||||
<li> GIT for version control, highly recommended</li>
|
||||
<li> Devilry for handing in projects, next week</li>
|
||||
<li> Anaconda and other Python environments, see intro slides</li>
|
||||
<li> Linear Regression and Logistic Regression;</li>
|
||||
<li> Neural networks and deep learning, including convolutional and recurrent neural networks</li>
|
||||
<li> Decisions trees, Random Forests, Bagging and Boosting</li>
|
||||
<li> Support vector machines</li>
|
||||
<li> Bayesian linear and logistic regression</li>
|
||||
<li> Boltzmann Machines</li>
|
||||
<li> Unsupervised learning Dimensionality reduction, from PCA to cluster models</li>
|
||||
</ul>
|
||||
|
||||
Hands-on demonstrations, exercises and projects aim at deepening your understanding of these topics.
|
||||
|
||||
<p>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
@@ -142,6 +153,7 @@ end of tocinfo -->
|
||||
<li><a href="._Intro2Course-bs008.html">9</a></li>
|
||||
<li class="active"><a href="._Intro2Course-bs009.html">10</a></li>
|
||||
<li><a href="._Intro2Course-bs010.html">11</a></li>
|
||||
<li><a href="._Intro2Course-bs011.html">12</a></li>
|
||||
<li><a href="._Intro2Course-bs010.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -44,26 +44,27 @@ Automatically generated HTML file from DocOnce source
|
||||
'sections': [('Overview of first week', 2, None, '___sec0'),
|
||||
('Lectures and ComputerLab', 2, None, '___sec1'),
|
||||
('Course Format', 2, None, '___sec2'),
|
||||
('Teachers and ComputerLab', 2, None, '___sec3'),
|
||||
('Teachers', 2, None, '___sec3'),
|
||||
('Deadlines for projects (tentative)', 2, None, '___sec4'),
|
||||
('Learning outcomes', 2, None, '___sec5'),
|
||||
('Prerequisites', 2, None, '___sec5'),
|
||||
('Learning outcomes', 2, None, '___sec6'),
|
||||
('Topics covered in this course: Statistical analysis and '
|
||||
'optimization of data',
|
||||
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|
||||
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|
||||
'___sec6'),
|
||||
'___sec7'),
|
||||
('Topics covered in this course: Machine Learning',
|
||||
2,
|
||||
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|
||||
'___sec7'),
|
||||
'___sec8'),
|
||||
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|
||||
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|
||||
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|
||||
'___sec8'),
|
||||
'___sec9'),
|
||||
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|
||||
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|
||||
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|
||||
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|
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end of tocinfo -->
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|
||||
<body>
|
||||
@@ -88,13 +89,14 @@ end of tocinfo -->
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs001.html#___sec0" style="font-size: 80%;">Overview of first week</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs002.html#___sec1" style="font-size: 80%;">Lectures and ComputerLab</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs003.html#___sec2" style="font-size: 80%;">Course Format</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers and ComputerLab</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs005.html#___sec4" style="font-size: 80%;">Deadlines for projects (tentative)</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Learning outcomes</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec9" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Prerequisites</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Learning outcomes</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec9" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs011.html#___sec10" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -110,26 +112,22 @@ end of tocinfo -->
|
||||
<a name="part0010"></a>
|
||||
<!-- !split -->
|
||||
|
||||
<h2 id="___sec9" class="anchor">Other courses on Data science and Machine Learning at UiO </h2>
|
||||
<h2 id="___sec9" class="anchor">Extremely useful tools, strongly recommended </h2>
|
||||
|
||||
<p>
|
||||
The link here <a href="https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/" target="_self"><tt>https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/</tt></a> gives an excellent overview of courses on Machine learning at UiO.
|
||||
<div class="panel panel-default">
|
||||
<div class="panel-body">
|
||||
<p> <!-- subsequent paragraphs come in larger fonts, so start with a paragraph -->
|
||||
|
||||
<ol>
|
||||
<li> <a href="http://www.uio.no/studier/emner/matnat/math/STK2100/index-eng.html" target="_self">STK2100 Machine learning and statistical methods for prediction and classification</a>.</li>
|
||||
<li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN3050/index-eng.html" target="_self">IN3050 Introduction to Artificial Intelligence and Machine Learning</a>. Introductory course in machine learning and AI with an algorithmic approach.</li>
|
||||
<li> <a href="http://www.uio.no/studier/emner/matnat/math/STK-INF3000/index-eng.html" target="_self">STK-INF3000/4000 Selected Topics in Data Science</a>. The course provides insight into selected contemporary relevant topics within Data Science.</li>
|
||||
<li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN4080/index.html" target="_self">IN4080 Natural Language Processing</a>. Probabilistic and machine learning techniques applied to natural language processing.</li>
|
||||
<li> <a href="https://www.uio.no/studier/emner/matnat/math/STK-IN4300/index-eng.html" target="_self">STK-IN4300 Statistical learning methods in Data Science</a>. An advanced introduction to statistical and machine learning. For students with a good mathematics and statistics background.</li>
|
||||
<li> <a href="http://www.uio.no/studier/emner/matnat/ifi/INF4490/" target="_self">INF4490 Biologically Inspired Computing</a>. An introduction to self-adapting methods also called artificial intelligence or machine learning.</li>
|
||||
<li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN-STK5000/index-eng.html" target="_self">IN-STK5000 Adaptive Methods for Data-Based Decision Making</a>. Methods for adaptive collection and processing of data based on machine learning techniques.</li>
|
||||
<li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN5400/" target="_self">IN5400/INF5860 Machine Learning for Image Analysis</a>. An introduction to deep learning with particular emphasis on applications within Image analysis, but useful for other application areas too.</li>
|
||||
<li> <a href="https://www.uio.no/studier/emner/matnat/its/TEK5040/" target="_self">TEK5040 Deep learning for autonomous systems</a>. The course addresses advanced algorithms and architectures for deep learning with neural networks. The course provides an introduction to how deep-learning techniques can be used in the construction of key parts of advanced autonomous systems that exist in physical environments and cyber environments.</li>
|
||||
<li> <a href="https://www.uio.no/studier/emner/matnat/math/STK4051/index-eng.html" target="_self">STK4051 Computational Statistics</a></li>
|
||||
<li> <a href="https://www.uio.no/studier/emner/matnat/math/STK4021/index-eng.html" target="_self">STK4021 Applied Bayesian Analysis and Numerical Methods</a></li>
|
||||
</ol>
|
||||
<ul>
|
||||
<li> GIT for version control, and GitHub or GitLab as repositories, highly recommended. This will be discussed during the first exercise session</li>
|
||||
<li> Anaconda and other Python environments, see intro slides and first exercise session</li>
|
||||
</ul>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
|
||||
<p>
|
||||
<p>
|
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||||
@@ -145,6 +143,8 @@ The link here <a href="https://www.mn.uio.no/english/research/about/centre-focus
|
||||
<li><a href="._Intro2Course-bs008.html">9</a></li>
|
||||
<li><a href="._Intro2Course-bs009.html">10</a></li>
|
||||
<li class="active"><a href="._Intro2Course-bs010.html">11</a></li>
|
||||
<li><a href="._Intro2Course-bs011.html">12</a></li>
|
||||
<li><a href="._Intro2Course-bs011.html">»</a></li>
|
||||
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|
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||||
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||||
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|
||||
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|
||||
<meta name="description" content="Applied Data Analysis and Machine Learning: Introduction to the course, Logistics and Practicalities">
|
||||
|
||||
<title>Applied Data Analysis and Machine Learning: Introduction to the course, Logistics and Practicalities</title>
|
||||
|
||||
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|
||||
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||||
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||||
<!-- tocinfo
|
||||
{'highest level': 2,
|
||||
'sections': [('Overview of first week', 2, None, '___sec0'),
|
||||
('Lectures and ComputerLab', 2, None, '___sec1'),
|
||||
('Course Format', 2, None, '___sec2'),
|
||||
('Teachers', 2, None, '___sec3'),
|
||||
('Deadlines for projects (tentative)', 2, None, '___sec4'),
|
||||
('Prerequisites', 2, None, '___sec5'),
|
||||
('Learning outcomes', 2, None, '___sec6'),
|
||||
('Topics covered in this course: Statistical analysis and '
|
||||
'optimization of data',
|
||||
2,
|
||||
None,
|
||||
'___sec7'),
|
||||
('Topics covered in this course: Machine Learning',
|
||||
2,
|
||||
None,
|
||||
'___sec8'),
|
||||
('Extremely useful tools, strongly recommended',
|
||||
2,
|
||||
None,
|
||||
'___sec9'),
|
||||
('Other courses on Data science and Machine Learning at UiO',
|
||||
2,
|
||||
None,
|
||||
'___sec10')]}
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<a class="navbar-brand" href="Intro2Course-bs.html">Applied Data Analysis and Machine Learning: Introduction to the course, Logistics and Practicalities</a>
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<ul class="nav navbar-nav navbar-right">
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<a href="#" class="dropdown-toggle" data-toggle="dropdown">Contents <b class="caret"></b></a>
|
||||
<ul class="dropdown-menu">
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs001.html#___sec0" style="font-size: 80%;">Overview of first week</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs002.html#___sec1" style="font-size: 80%;">Lectures and ComputerLab</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs003.html#___sec2" style="font-size: 80%;">Course Format</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs005.html#___sec4" style="font-size: 80%;">Deadlines for projects (tentative)</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Prerequisites</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Learning outcomes</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec10" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
|
||||
</div>
|
||||
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||||
<p> </p><p> </p><p> </p> <!-- add vertical space -->
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||||
|
||||
<a name="part0011"></a>
|
||||
<!-- !split -->
|
||||
|
||||
<h2 id="___sec10" class="anchor">Other courses on Data science and Machine Learning at UiO </h2>
|
||||
|
||||
<p>
|
||||
The link here <a href="https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/" target="_self"><tt>https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/</tt></a> gives an excellent overview of courses on Machine learning at UiO.
|
||||
|
||||
<ol>
|
||||
<li> <a href="http://www.uio.no/studier/emner/matnat/math/STK2100/index-eng.html" target="_self">STK2100 Machine learning and statistical methods for prediction and classification</a>.</li>
|
||||
<li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN3050/index-eng.html" target="_self">IN3050 Introduction to Artificial Intelligence and Machine Learning</a>. Introductory course in machine learning and AI with an algorithmic approach.</li>
|
||||
<li> <a href="http://www.uio.no/studier/emner/matnat/math/STK-INF3000/index-eng.html" target="_self">STK-INF3000/4000 Selected Topics in Data Science</a>. The course provides insight into selected contemporary relevant topics within Data Science.</li>
|
||||
<li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN4080/index.html" target="_self">IN4080 Natural Language Processing</a>. Probabilistic and machine learning techniques applied to natural language processing.</li>
|
||||
<li> <a href="https://www.uio.no/studier/emner/matnat/math/STK-IN4300/index-eng.html" target="_self">STK-IN4300 Statistical learning methods in Data Science</a>. An advanced introduction to statistical and machine learning. For students with a good mathematics and statistics background.</li>
|
||||
<li> <a href="http://www.uio.no/studier/emner/matnat/ifi/INF4490/" target="_self">INF4490 Biologically Inspired Computing</a>. An introduction to self-adapting methods also called artificial intelligence or machine learning.</li>
|
||||
<li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN-STK5000/index-eng.html" target="_self">IN-STK5000 Adaptive Methods for Data-Based Decision Making</a>. Methods for adaptive collection and processing of data based on machine learning techniques.</li>
|
||||
<li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN5400/" target="_self">IN5400/INF5860 Machine Learning for Image Analysis</a>. An introduction to deep learning with particular emphasis on applications within Image analysis, but useful for other application areas too.</li>
|
||||
<li> <a href="https://www.uio.no/studier/emner/matnat/its/TEK5040/" target="_self">TEK5040 Deep learning for autonomous systems</a>. The course addresses advanced algorithms and architectures for deep learning with neural networks. The course provides an introduction to how deep-learning techniques can be used in the construction of key parts of advanced autonomous systems that exist in physical environments and cyber environments.</li>
|
||||
<li> <a href="https://www.uio.no/studier/emner/matnat/math/STK4051/index-eng.html" target="_self">STK4051 Computational Statistics</a></li>
|
||||
<li> <a href="https://www.uio.no/studier/emner/matnat/math/STK4021/index-eng.html" target="_self">STK4021 Applied Bayesian Analysis and Numerical Methods</a></li>
|
||||
</ol>
|
||||
|
||||
|
||||
<p>
|
||||
<!-- navigation buttons at the bottom of the page -->
|
||||
<ul class="pagination">
|
||||
<li><a href="._Intro2Course-bs010.html">«</a></li>
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||||
<li><a href="._Intro2Course-bs000.html">1</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._Intro2Course-bs003.html">4</a></li>
|
||||
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|
||||
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<li><a href="._Intro2Course-bs007.html">8</a></li>
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<li><a href="._Intro2Course-bs008.html">9</a></li>
|
||||
<li><a href="._Intro2Course-bs009.html">10</a></li>
|
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<li><a href="._Intro2Course-bs010.html">11</a></li>
|
||||
<li class="active"><a href="._Intro2Course-bs011.html">12</a></li>
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||||
@@ -44,26 +44,27 @@ Automatically generated HTML file from DocOnce source
|
||||
'sections': [('Overview of first week', 2, None, '___sec0'),
|
||||
('Lectures and ComputerLab', 2, None, '___sec1'),
|
||||
('Course Format', 2, None, '___sec2'),
|
||||
('Teachers and ComputerLab', 2, None, '___sec3'),
|
||||
('Teachers', 2, None, '___sec3'),
|
||||
('Deadlines for projects (tentative)', 2, None, '___sec4'),
|
||||
('Learning outcomes', 2, None, '___sec5'),
|
||||
('Prerequisites', 2, None, '___sec5'),
|
||||
('Learning outcomes', 2, None, '___sec6'),
|
||||
('Topics covered in this course: Statistical analysis and '
|
||||
'optimization of data',
|
||||
2,
|
||||
None,
|
||||
'___sec6'),
|
||||
'___sec7'),
|
||||
('Topics covered in this course: Machine Learning',
|
||||
2,
|
||||
None,
|
||||
'___sec7'),
|
||||
'___sec8'),
|
||||
('Extremely useful tools, strongly recommended',
|
||||
2,
|
||||
None,
|
||||
'___sec8'),
|
||||
'___sec9'),
|
||||
('Other courses on Data science and Machine Learning at UiO',
|
||||
2,
|
||||
None,
|
||||
'___sec9')]}
|
||||
'___sec10')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -88,13 +89,14 @@ end of tocinfo -->
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs001.html#___sec0" style="font-size: 80%;">Overview of first week</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs002.html#___sec1" style="font-size: 80%;">Lectures and ComputerLab</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs003.html#___sec2" style="font-size: 80%;">Course Format</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers and ComputerLab</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs004.html#___sec3" style="font-size: 80%;">Teachers</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs005.html#___sec4" style="font-size: 80%;">Deadlines for projects (tentative)</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Learning outcomes</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs006.html#___sec5" style="font-size: 80%;">Prerequisites</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs007.html#___sec6" style="font-size: 80%;">Learning outcomes</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs008.html#___sec7" style="font-size: 80%;">Topics covered in this course: Statistical analysis and optimization of data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs009.html#___sec8" style="font-size: 80%;">Topics covered in this course: Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs010.html#___sec9" style="font-size: 80%;">Extremely useful tools, strongly recommended</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._Intro2Course-bs011.html#___sec10" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -129,7 +131,7 @@ end of tocinfo -->
|
||||
<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
|
||||
<br>
|
||||
<p>
|
||||
<center><h4>Nov 12, 2019</h4></center> <!-- date -->
|
||||
<center><h4>Aug 19, 2020</h4></center> <!-- date -->
|
||||
<br>
|
||||
<p>
|
||||
|
||||
@@ -153,7 +155,7 @@ end of tocinfo -->
|
||||
<li><a href="._Intro2Course-bs008.html">9</a></li>
|
||||
<li><a href="._Intro2Course-bs009.html">10</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._Intro2Course-bs010.html">11</a></li>
|
||||
<li><a href="._Intro2Course-bs011.html">12</a></li>
|
||||
<li><a href="._Intro2Course-bs001.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
@@ -171,7 +173,7 @@ end of tocinfo -->
|
||||
|
||||
|
||||
<center style="font-size:80%">
|
||||
<!-- copyright --> © 1999-2019, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
|
||||
<!-- copyright --> © 1999-2020, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
|
||||
</center>
|
||||
|
||||
|
||||
|
||||
@@ -132,12 +132,12 @@ td.padding {
|
||||
<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
|
||||
<br>
|
||||
<p> <br>
|
||||
<center><h4>Nov 12, 2019</h4></center> <!-- date -->
|
||||
<center><h4>Aug 19, 2020</h4></center> <!-- date -->
|
||||
<br>
|
||||
<p>
|
||||
|
||||
<center style="font-size:80%">
|
||||
<!-- copyright --> © 1999-2019, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
|
||||
<!-- copyright --> © 1999-2020, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
|
||||
</center>
|
||||
</section>
|
||||
|
||||
@@ -150,13 +150,13 @@ td.padding {
|
||||
<b></b>
|
||||
<ul>
|
||||
|
||||
<p><li> Thursday August 22: First lecture: Presentation of the course, aims and content</li>
|
||||
<p><li> Thursday August 20: First lecture: Presentation of the course, aims and content</li>
|
||||
|
||||
<p><li> Thursday: Second Lecture: Start with simple linear regression and repetition of linear algebra</li>
|
||||
<p><li> Thursday: Second Lecture: Start with simple linear regression and repetition of linear algebra and elements of statistics</li>
|
||||
|
||||
<p><li> Friday August 23: Linear regression</li>
|
||||
<p><li> Friday August 21: Linear regression</li>
|
||||
|
||||
<p><li> Computer lab: Tuesday. First time: Tuesday August 27.</li>
|
||||
<p><li> Computer lab: Wednesdays, 8am-6pm. First time: Wednesday August 26.</li>
|
||||
</ul>
|
||||
</div>
|
||||
</section>
|
||||
@@ -170,18 +170,14 @@ td.padding {
|
||||
<b></b>
|
||||
<ul>
|
||||
|
||||
<p><li> Lectures: Thursday (2.15pm-4pm, this may change) and Friday (12.15pm-2pm).</li>
|
||||
<p><li> Lectures: Thursday (12.15pm-2pm and Friday (12.15pm-2pm). Due to the present COVID-19 situation all lectures will be online. They will be recorded and posted online at the official UiO <a href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/index.html" target="_blank">website</a>.</li>
|
||||
|
||||
<p><li> Weekly reading assignments needed to solve projects and exercises.</li>
|
||||
<p><li> Weekly reading assignments and videos needed to solve projects and exercises.</li>
|
||||
|
||||
<p><li> Weekly exercises when not working on projects. You can hand in exercises if you want.</li>
|
||||
|
||||
<p><li> First hour of each lab session may be used to discuss technicalities, address questions etc linked with projects and exercises.</li>
|
||||
|
||||
<p><li> Detailed lecture notes, exercises, all programs presented, projects etc can be found at the homepage of the course.</li>
|
||||
|
||||
<p><li> Computerlab: Tuesday (8am-4pm), VB IT-auditorium 3. Depending on how many enlist we may extend the lab sessions</li>
|
||||
|
||||
<p><li> Weekly plans and all other information are on the official webpage.</li>
|
||||
|
||||
<p><li> No final exam, three projects that are graded and have to be approved.</li>
|
||||
@@ -219,7 +215,7 @@ td.padding {
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec3">Teachers and ComputerLab </h2>
|
||||
<h2 id="___sec3">Teachers </h2>
|
||||
|
||||
<p>
|
||||
<div class="alert alert-block alert-block alert-text-normal">
|
||||
@@ -227,27 +223,28 @@ td.padding {
|
||||
<p>
|
||||
<b>Teachers :</b>
|
||||
|
||||
<ol>
|
||||
<p><li> <a href="https://www.researchgate.net/profile/Hanna_Svennevik" target="_blank">Hanna Svennevik</a></li>
|
||||
<p><li> <a href="http://mhjgit.github.io/info/doc/web/" target="_blank">Morten Hjorth-Jensen</a></li>
|
||||
<p><li> <a href="https://no.linkedin.com/in/lucas-charpentier-176206171" target="_blank">Lucas Charpentier</a></li>
|
||||
<p><li> <a href="https://www.researchgate.net/profile/Stian_Bilek" target="_blank">Stian Bilek</a></li>
|
||||
<p><li> <a href="https://github.com/Schoyen" target="_blank">Øyvind Sigmundson Schøyen</a></li>
|
||||
</ol>
|
||||
<p>
|
||||
<ul>
|
||||
<p><li> Morten Hjorth-Jensen, morten.hjorth-jensen@fys.uio.no</li>
|
||||
|
||||
<table border="1">
|
||||
<thead>
|
||||
<tr><th align="center"> day </th> <th align="center"> Time </th> </tr>
|
||||
</thead>
|
||||
<tbody>
|
||||
<tr><td align="center"> Group 1: Tuesday </td> <td align="center"> 8am-10am </td> </tr>
|
||||
<tr><td align="center"> Group 2: Tuesday </td> <td align="center"> 10am-12pm </td> </tr>
|
||||
<tr><td align="center"> Group 3: Tuesday </td> <td align="center"> 12pm-2pm </td> </tr>
|
||||
<tr><td align="center"> Group 4: Tuesday </td> <td align="center"> 2pm-4pm </td> </tr>
|
||||
</tbody>
|
||||
</table>
|
||||
<ul>
|
||||
|
||||
<p><li> <b>Phone</b>: +47-48257387</li>
|
||||
|
||||
<p><li> <b>Office</b>: Department of Physics, University of Oslo, Eastern wing, room FØ470</li>
|
||||
|
||||
<p><li> <b>Office hours</b>: <em>Anytime</em>! In Fall Semester 2020 (FS20), as a rule of thumb office hours are planned via computer or telephone. Individual or group office hours will be performed via zoom. Feel free to send an email for planning. In person meetings may also be possible if allowed by the University of Oslo's COVID-19 instructions.</li>
|
||||
</ul>
|
||||
<p><li> Øyvind Sigmundson Schøyen, oyvinssc@student.matnat.uio.no</li>
|
||||
|
||||
<ul>
|
||||
|
||||
<p><li> <b>Office</b>: Department of Physics, University of Oslo, Eastern wing, room FØ452</li>
|
||||
</ul>
|
||||
<p><li> Michael Bitney, m.s.bitney@fys.uio.no</li>
|
||||
<p><li> Kristian Wold, kriswold@student.matnat.uio.no</li>
|
||||
<p><li> Nicolai Haug, nicoha@student.matnat.uio.no</li>
|
||||
<p><li> Per-Dimitri Sønsteland, perdimitri.bs@gmail.com</li>
|
||||
</ul>
|
||||
</div>
|
||||
</section>
|
||||
|
||||
@@ -259,13 +256,13 @@ td.padding {
|
||||
<div class="alert alert-block alert-block alert-text-normal">
|
||||
<b></b>
|
||||
<ol>
|
||||
<p><li> Project 1: September 30 (graded with feedback)</li>
|
||||
<p><li> Project 2: November 13 (graded with feedback)</li>
|
||||
<p><li> Project 3: December 15 (graded with feedback)</li>
|
||||
<p><li> Project 1: September 28 (graded with feedback)</li>
|
||||
<p><li> Project 2: November 2 (graded with feedback)</li>
|
||||
<p><li> Project 3: December 7 (graded with feedback)</li>
|
||||
</ol>
|
||||
<p>
|
||||
|
||||
Projects are handed in using devilry.ifi.uio.no. We use Github as repository for codes, benchmark calculations etc. Comments and feedback on projects only via devilry.
|
||||
Projects are handed in using <b>Canvas</b>. We use Github as repository for codes, benchmark calculations etc. Comments and feedback on projects only via <b>Canvas</b>.
|
||||
|
||||
|
||||
</div>
|
||||
@@ -273,49 +270,81 @@ Projects are handed in using devilry.ifi.uio.no. We use Github as repository for
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec5">Learning outcomes </h2>
|
||||
<h2 id="___sec5">Prerequisites </h2>
|
||||
|
||||
<p>
|
||||
Basic knowledge in programming and mathematics, with an emphasis on
|
||||
linear algebra. Knowledge of Python or/and C++ as programming
|
||||
languages is strongly recommended and experience with Jupiter notebook
|
||||
is recommended. Required courses are the equivalents to the University
|
||||
of Oslo mathematics courses MAT1100, MAT1110, MAT1120 and at least one
|
||||
of the corresponding computing and programming courses INF1000/INF1110
|
||||
or MAT-INF1100/MAT-INF1100L/BIOS1100/KJM-INF1100. Most universities
|
||||
offer nowadays a basic programming course (often compulsory) where
|
||||
Python is the recurring programming language.
|
||||
</section>
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec6">Learning outcomes </h2>
|
||||
|
||||
<p>
|
||||
<div class="alert alert-block alert-block alert-text-normal">
|
||||
<b></b>
|
||||
<p>
|
||||
This course aims at giving you insights and knowledge about many of the central algorithms used in Data Analysis and Machine Learning. The course is project based and through various numerical projects, normally three, you will be exposed to fundamental research problems in these fields, with the aim to reproduce state of the art scientific results. Both supervised and unsupervised methods will be covered. The emphasis is on a frequentist approach, although we will try to link it with a Bayesian approach as well. You will learn to develop and structure large codes for studying different cases where Machine Learning is applied to, get acquainted with computing facilities and learn to handle large scientific projects. A good scientific and ethical conduct is emphasized throughout the course. More specifically, after this course you will
|
||||
|
||||
<ul>
|
||||
<p><li> Learn about basic data analysis, statistical analysis, Bayesian statistics, Monte Carlo sampling, data optimization and machine learning</li>
|
||||
<p><li> Be capable of extending the acquired knowledge to other systems and cases</li>
|
||||
<p><li> Have an understanding of central algorithms used in data analysis and machine learning</li>
|
||||
<p><li> Gain knowledge of central aspects of Monte Carlo methods, Markov chains, Gibbs samplers and their possible applications</li>
|
||||
<p><li> Understand linear methods for regression and classification, from ordinary least squares, via Lasso and Ridge to Logistic regression</li>
|
||||
<p><li> Learn about various neural networks and deep learning methods for supervised and unsupervised learning</li>
|
||||
<p><li> Learn about about decision trees and random forests</li>
|
||||
<p><li> Learn about support vector machines and kernel transformations</li>
|
||||
<p><li> Reduction of data sets, from PCA to clustering, supervised and unsupervided methods</li>
|
||||
<p><li> Work on numerical projects to illustrate the theory. The projects play a central role and you are expected to know modern programming languages like Python or C++</li>
|
||||
<p><li> Learn about basic data analysis, statistical analysis, Bayesian statistics, Monte Carlo sampling, data optimization and machine learning;</li>
|
||||
<p><li> Be capable of extending the acquired knowledge to other systems and cases;</li>
|
||||
<p><li> Have an understanding of central algorithms used in data analysis and machine learning;</li>
|
||||
<p><li> Understand linear methods for regression and classification, from ordinary least squares, via Lasso and Ridge to Logistic regression;</li>
|
||||
<p><li> Learn about neural networks and deep learning methods for supervised and unsupervised learning. Emphasis on feed forward neural networks, convolutional and recurrent neural networks;</li>
|
||||
<p><li> Learn about about decision trees, random forests, bagging and boosting methods;</li>
|
||||
<p><li> Learn about support vector machines and kernel transformations;</li>
|
||||
<p><li> Reduction of data sets, from PCA to clustering;</li>
|
||||
<p><li> Autoencoders and Reinforcement Learning;</li>
|
||||
<p><li> Work on numerical projects to illustrate the theory. The projects play a central role and you are expected to know modern programming languages like Python or C++ and/or Fortran (Fortran2003 or later).</li>
|
||||
</ul>
|
||||
</div>
|
||||
</section>
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec6">Topics covered in this course: Statistical analysis and optimization of data </h2>
|
||||
<h2 id="___sec7">Topics covered in this course: Statistical analysis and optimization of data </h2>
|
||||
|
||||
<p>
|
||||
The course has two central parts
|
||||
|
||||
<ol>
|
||||
<p><li> Statistical analysis and optimization of data</li>
|
||||
<p><li> Machine learning</li>
|
||||
</ol>
|
||||
<p>
|
||||
|
||||
These topics will be scattered thorughout the course and may not necessarily be taught separately. Rather, we will often take an approach (during the lectures and project/exercise sessions) where say elements from statistical data analysis are mixed with specific Machine Learning algorithms
|
||||
|
||||
<p>
|
||||
<div class="alert alert-block alert-block alert-text-normal">
|
||||
<b></b>
|
||||
<b>Statistical analysis and optimization of data.</b>
|
||||
<p>
|
||||
The following topics will be covered
|
||||
|
||||
<ul>
|
||||
<p><li> Basic concepts, expectation values, variance, covariance, correlation functions and errors</li>
|
||||
<p><li> Simpler models, binomial distribution, the Poisson distribution, simple and multivariate normal distributions</li>
|
||||
<p><li> Central elements of Bayesian statistics and modeling</li>
|
||||
<p><li> Gradient methods for data optimization</li>
|
||||
<p><li> Monte Carlo methods, Markov chains, Metropolis-Hastings algorithm</li>
|
||||
<p><li> Linear methods for regression and classification</li>
|
||||
<p><li> Estimation of errors using cross-validation, blocking, bootstrapping and jackknife methods</li>
|
||||
<p><li> Practical optimization using Singular-value decomposition and least squares for parameterizing data</li>
|
||||
<p><li> Basic concepts, expectation values, variance, covariance, correlation functions and errors;</li>
|
||||
<p><li> Simpler models, binomial distribution, the Poisson distribution, simple and multivariate normal distributions;</li>
|
||||
<p><li> Central elements of Bayesian statistics and modeling;</li>
|
||||
<p><li> Gradient methods for data optimization,</li>
|
||||
<p><li> Monte Carlo methods, Markov chains, Gibbs sampling and Metropolis-Hastings sampling;</li>
|
||||
<p><li> Estimation of errors and resampling techniques such as the cross-validation, blocking, bootstrapping and jackknife methods;</li>
|
||||
<p><li> Principal Component Analysis (PCA) and its mathematical foundation</li>
|
||||
</ul>
|
||||
</div>
|
||||
</section>
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec7">Topics covered in this course: Machine Learning </h2>
|
||||
<h2 id="___sec8">Topics covered in this course: Machine Learning </h2>
|
||||
|
||||
<p>
|
||||
<div class="alert alert-block alert-block alert-text-normal">
|
||||
@@ -324,38 +353,41 @@ Projects are handed in using devilry.ifi.uio.no. We use Github as repository for
|
||||
The following topics will be covered
|
||||
|
||||
<ul>
|
||||
<p><li> Linear Regression and Logistic Regression</li>
|
||||
<p><li> Neural networks and deep learning</li>
|
||||
<p><li> Decisions trees and nearest neighbor algorithms</li>
|
||||
<p><li> Linear Regression and Logistic Regression;</li>
|
||||
<p><li> Neural networks and deep learning, including convolutional and recurrent neural networks</li>
|
||||
<p><li> Decisions trees, Random Forests, Bagging and Boosting</li>
|
||||
<p><li> Support vector machines</li>
|
||||
<p><li> Bayesian Neural Networks</li>
|
||||
<p><li> Bayesian linear and logistic regression</li>
|
||||
<p><li> Boltzmann Machines</li>
|
||||
<p><li> Dimensionality reduction, from PCA to cluster models</li>
|
||||
<p><li> Unsupervised learning Dimensionality reduction, from PCA to cluster models</li>
|
||||
</ul>
|
||||
<p>
|
||||
|
||||
Hands-on demonstrations, exercises and projects aim at deepening your understanding of these topics.
|
||||
|
||||
|
||||
</div>
|
||||
</section>
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec8">Extremely useful tools, strongly recommended </h2>
|
||||
<h2 id="___sec9">Extremely useful tools, strongly recommended </h2>
|
||||
|
||||
<p>
|
||||
<div class="alert alert-block alert-block alert-text-normal">
|
||||
<b>and discussed at the lab sessions.</b>
|
||||
<ul>
|
||||
|
||||
<p><li> GIT for version control, highly recommended</li>
|
||||
<p><li> GIT for version control, and GitHub or GitLab as repositories, highly recommended. This will be discussed during the first exercise session</li>
|
||||
|
||||
<p><li> Devilry for handing in projects, next week</li>
|
||||
|
||||
<p><li> Anaconda and other Python environments, see intro slides</li>
|
||||
<p><li> Anaconda and other Python environments, see intro slides and first exercise session</li>
|
||||
</ul>
|
||||
</div>
|
||||
</section>
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec9">Other courses on Data science and Machine Learning at UiO </h2>
|
||||
<h2 id="___sec10">Other courses on Data science and Machine Learning at UiO </h2>
|
||||
|
||||
<p>
|
||||
The link here <a href="https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/" target="_blank"><tt>https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/</tt></a> gives an excellent overview of courses on Machine learning at UiO.
|
||||
|
||||
@@ -64,26 +64,27 @@ div { text-align: justify; text-justify: inter-word; }
|
||||
'sections': [('Overview of first week', 2, None, '___sec0'),
|
||||
('Lectures and ComputerLab', 2, None, '___sec1'),
|
||||
('Course Format', 2, None, '___sec2'),
|
||||
('Teachers and ComputerLab', 2, None, '___sec3'),
|
||||
('Teachers', 2, None, '___sec3'),
|
||||
('Deadlines for projects (tentative)', 2, None, '___sec4'),
|
||||
('Learning outcomes', 2, None, '___sec5'),
|
||||
('Prerequisites', 2, None, '___sec5'),
|
||||
('Learning outcomes', 2, None, '___sec6'),
|
||||
('Topics covered in this course: Statistical analysis and '
|
||||
'optimization of data',
|
||||
2,
|
||||
None,
|
||||
'___sec6'),
|
||||
'___sec7'),
|
||||
('Topics covered in this course: Machine Learning',
|
||||
2,
|
||||
None,
|
||||
'___sec7'),
|
||||
'___sec8'),
|
||||
('Extremely useful tools, strongly recommended',
|
||||
2,
|
||||
None,
|
||||
'___sec8'),
|
||||
'___sec9'),
|
||||
('Other courses on Data science and Machine Learning at UiO',
|
||||
2,
|
||||
None,
|
||||
'___sec9')]}
|
||||
'___sec10')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -109,7 +110,7 @@ end of tocinfo -->
|
||||
<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
|
||||
<br>
|
||||
<p>
|
||||
<center><h4>Nov 12, 2019</h4></center> <!-- date -->
|
||||
<center><h4>Aug 19, 2020</h4></center> <!-- date -->
|
||||
<br>
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
@@ -122,10 +123,10 @@ end of tocinfo -->
|
||||
<p>
|
||||
|
||||
<ul>
|
||||
<li> Thursday August 22: First lecture: Presentation of the course, aims and content</li>
|
||||
<li> Thursday: Second Lecture: Start with simple linear regression and repetition of linear algebra</li>
|
||||
<li> Friday August 23: Linear regression</li>
|
||||
<li> Computer lab: Tuesday. First time: Tuesday August 27.</li>
|
||||
<li> Thursday August 20: First lecture: Presentation of the course, aims and content</li>
|
||||
<li> Thursday: Second Lecture: Start with simple linear regression and repetition of linear algebra and elements of statistics</li>
|
||||
<li> Friday August 21: Linear regression</li>
|
||||
<li> Computer lab: Wednesdays, 8am-6pm. First time: Wednesday August 26.</li>
|
||||
</ul>
|
||||
</div>
|
||||
|
||||
@@ -141,12 +142,10 @@ end of tocinfo -->
|
||||
<p>
|
||||
|
||||
<ul>
|
||||
<li> Lectures: Thursday (2.15pm-4pm, this may change) and Friday (12.15pm-2pm).</li>
|
||||
<li> Weekly reading assignments needed to solve projects and exercises.</li>
|
||||
<li> Lectures: Thursday (12.15pm-2pm and Friday (12.15pm-2pm). Due to the present COVID-19 situation all lectures will be online. They will be recorded and posted online at the official UiO <a href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/index.html" target="_blank">website</a>.</li>
|
||||
<li> Weekly reading assignments and videos needed to solve projects and exercises.</li>
|
||||
<li> Weekly exercises when not working on projects. You can hand in exercises if you want.</li>
|
||||
<li> First hour of each lab session may be used to discuss technicalities, address questions etc linked with projects and exercises.</li>
|
||||
<li> Detailed lecture notes, exercises, all programs presented, projects etc can be found at the homepage of the course.</li>
|
||||
<li> Computerlab: Tuesday (8am-4pm), VB IT-auditorium 3. Depending on how many enlist we may extend the lab sessions</li>
|
||||
<li> Weekly plans and all other information are on the official webpage.</li>
|
||||
<li> No final exam, three projects that are graded and have to be approved.</li>
|
||||
</ul>
|
||||
@@ -182,7 +181,7 @@ end of tocinfo -->
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec3">Teachers and ComputerLab </h2>
|
||||
<h2 id="___sec3">Teachers </h2>
|
||||
|
||||
<p>
|
||||
<div class="alert alert-block alert-block alert-text-normal">
|
||||
@@ -192,26 +191,26 @@ end of tocinfo -->
|
||||
<p>
|
||||
<b>Teachers :</b>
|
||||
|
||||
<ol>
|
||||
<li> <a href="https://www.researchgate.net/profile/Hanna_Svennevik" target="_blank">Hanna Svennevik</a></li>
|
||||
<li> <a href="http://mhjgit.github.io/info/doc/web/" target="_blank">Morten Hjorth-Jensen</a></li>
|
||||
<li> <a href="https://no.linkedin.com/in/lucas-charpentier-176206171" target="_blank">Lucas Charpentier</a></li>
|
||||
<li> <a href="https://www.researchgate.net/profile/Stian_Bilek" target="_blank">Stian Bilek</a></li>
|
||||
<li> <a href="https://github.com/Schoyen" target="_blank">Øyvind Sigmundson Schøyen</a></li>
|
||||
</ol>
|
||||
<ul>
|
||||
<li> Morten Hjorth-Jensen, morten.hjorth-jensen@fys.uio.no</li>
|
||||
|
||||
<table border="1">
|
||||
<thead>
|
||||
<tr><th align="center"> day </th> <th align="center"> Time </th> </tr>
|
||||
</thead>
|
||||
<tbody>
|
||||
<tr><td align="center"> Group 1: Tuesday </td> <td align="center"> 8am-10am </td> </tr>
|
||||
<tr><td align="center"> Group 2: Tuesday </td> <td align="center"> 10am-12pm </td> </tr>
|
||||
<tr><td align="center"> Group 3: Tuesday </td> <td align="center"> 12pm-2pm </td> </tr>
|
||||
<tr><td align="center"> Group 4: Tuesday </td> <td align="center"> 2pm-4pm </td> </tr>
|
||||
</tbody>
|
||||
</table>
|
||||
<ul>
|
||||
<li> <b>Phone</b>: +47-48257387</li>
|
||||
<li> <b>Office</b>: Department of Physics, University of Oslo, Eastern wing, room FØ470</li>
|
||||
<li> <b>Office hours</b>: <em>Anytime</em>! In Fall Semester 2020 (FS20), as a rule of thumb office hours are planned via computer or telephone. Individual or group office hours will be performed via zoom. Feel free to send an email for planning. In person meetings may also be possible if allowed by the University of Oslo's COVID-19 instructions.</li>
|
||||
</ul>
|
||||
|
||||
<li> Øyvind Sigmundson Schøyen, oyvinssc@student.matnat.uio.no</li>
|
||||
|
||||
<ul>
|
||||
<li> <b>Office</b>: Department of Physics, University of Oslo, Eastern wing, room FØ452</li>
|
||||
</ul>
|
||||
|
||||
<li> Michael Bitney, m.s.bitney@fys.uio.no</li>
|
||||
<li> Kristian Wold, kriswold@student.matnat.uio.no</li>
|
||||
<li> Nicolai Haug, nicoha@student.matnat.uio.no</li>
|
||||
<li> Per-Dimitri Sønsteland, perdimitri.bs@gmail.com</li>
|
||||
</ul>
|
||||
</div>
|
||||
|
||||
|
||||
@@ -226,12 +225,12 @@ end of tocinfo -->
|
||||
<p>
|
||||
|
||||
<ol>
|
||||
<li> Project 1: September 30 (graded with feedback)</li>
|
||||
<li> Project 2: November 13 (graded with feedback)</li>
|
||||
<li> Project 3: December 15 (graded with feedback)</li>
|
||||
<li> Project 1: September 28 (graded with feedback)</li>
|
||||
<li> Project 2: November 2 (graded with feedback)</li>
|
||||
<li> Project 3: December 7 (graded with feedback)</li>
|
||||
</ol>
|
||||
|
||||
Projects are handed in using devilry.ifi.uio.no. We use Github as repository for codes, benchmark calculations etc. Comments and feedback on projects only via devilry.
|
||||
Projects are handed in using <b>Canvas</b>. We use Github as repository for codes, benchmark calculations etc. Comments and feedback on projects only via <b>Canvas</b>.
|
||||
|
||||
|
||||
</div>
|
||||
@@ -240,24 +239,43 @@ Projects are handed in using devilry.ifi.uio.no. We use Github as repository for
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec5">Learning outcomes </h2>
|
||||
<h2 id="___sec5">Prerequisites </h2>
|
||||
|
||||
<p>
|
||||
Basic knowledge in programming and mathematics, with an emphasis on
|
||||
linear algebra. Knowledge of Python or/and C++ as programming
|
||||
languages is strongly recommended and experience with Jupiter notebook
|
||||
is recommended. Required courses are the equivalents to the University
|
||||
of Oslo mathematics courses MAT1100, MAT1110, MAT1120 and at least one
|
||||
of the corresponding computing and programming courses INF1000/INF1110
|
||||
or MAT-INF1100/MAT-INF1100L/BIOS1100/KJM-INF1100. Most universities
|
||||
offer nowadays a basic programming course (often compulsory) where
|
||||
Python is the recurring programming language.
|
||||
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec6">Learning outcomes </h2>
|
||||
|
||||
<p>
|
||||
<div class="alert alert-block alert-block alert-text-normal">
|
||||
<b></b>
|
||||
<p>
|
||||
|
||||
<p>
|
||||
This course aims at giving you insights and knowledge about many of the central algorithms used in Data Analysis and Machine Learning. The course is project based and through various numerical projects, normally three, you will be exposed to fundamental research problems in these fields, with the aim to reproduce state of the art scientific results. Both supervised and unsupervised methods will be covered. The emphasis is on a frequentist approach, although we will try to link it with a Bayesian approach as well. You will learn to develop and structure large codes for studying different cases where Machine Learning is applied to, get acquainted with computing facilities and learn to handle large scientific projects. A good scientific and ethical conduct is emphasized throughout the course. More specifically, after this course you will
|
||||
|
||||
<ul>
|
||||
<li> Learn about basic data analysis, statistical analysis, Bayesian statistics, Monte Carlo sampling, data optimization and machine learning</li>
|
||||
<li> Be capable of extending the acquired knowledge to other systems and cases</li>
|
||||
<li> Have an understanding of central algorithms used in data analysis and machine learning</li>
|
||||
<li> Gain knowledge of central aspects of Monte Carlo methods, Markov chains, Gibbs samplers and their possible applications</li>
|
||||
<li> Understand linear methods for regression and classification, from ordinary least squares, via Lasso and Ridge to Logistic regression</li>
|
||||
<li> Learn about various neural networks and deep learning methods for supervised and unsupervised learning</li>
|
||||
<li> Learn about about decision trees and random forests</li>
|
||||
<li> Learn about support vector machines and kernel transformations</li>
|
||||
<li> Reduction of data sets, from PCA to clustering, supervised and unsupervided methods</li>
|
||||
<li> Work on numerical projects to illustrate the theory. The projects play a central role and you are expected to know modern programming languages like Python or C++</li>
|
||||
<li> Learn about basic data analysis, statistical analysis, Bayesian statistics, Monte Carlo sampling, data optimization and machine learning;</li>
|
||||
<li> Be capable of extending the acquired knowledge to other systems and cases;</li>
|
||||
<li> Have an understanding of central algorithms used in data analysis and machine learning;</li>
|
||||
<li> Understand linear methods for regression and classification, from ordinary least squares, via Lasso and Ridge to Logistic regression;</li>
|
||||
<li> Learn about neural networks and deep learning methods for supervised and unsupervised learning. Emphasis on feed forward neural networks, convolutional and recurrent neural networks;</li>
|
||||
<li> Learn about about decision trees, random forests, bagging and boosting methods;</li>
|
||||
<li> Learn about support vector machines and kernel transformations;</li>
|
||||
<li> Reduction of data sets, from PCA to clustering;</li>
|
||||
<li> Autoencoders and Reinforcement Learning;</li>
|
||||
<li> Work on numerical projects to illustrate the theory. The projects play a central role and you are expected to know modern programming languages like Python or C++ and/or Fortran (Fortran2003 or later).</li>
|
||||
</ul>
|
||||
</div>
|
||||
|
||||
@@ -265,22 +283,34 @@ Projects are handed in using devilry.ifi.uio.no. We use Github as repository for
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec6">Topics covered in this course: Statistical analysis and optimization of data </h2>
|
||||
<h2 id="___sec7">Topics covered in this course: Statistical analysis and optimization of data </h2>
|
||||
|
||||
<p>
|
||||
The course has two central parts
|
||||
|
||||
<ol>
|
||||
<li> Statistical analysis and optimization of data</li>
|
||||
<li> Machine learning</li>
|
||||
</ol>
|
||||
|
||||
These topics will be scattered thorughout the course and may not necessarily be taught separately. Rather, we will often take an approach (during the lectures and project/exercise sessions) where say elements from statistical data analysis are mixed with specific Machine Learning algorithms
|
||||
|
||||
<p>
|
||||
<div class="alert alert-block alert-block alert-text-normal">
|
||||
<b></b>
|
||||
<b>Statistical analysis and optimization of data.</b>
|
||||
<p>
|
||||
|
||||
<p>
|
||||
The following topics will be covered
|
||||
|
||||
<ul>
|
||||
<li> Basic concepts, expectation values, variance, covariance, correlation functions and errors</li>
|
||||
<li> Simpler models, binomial distribution, the Poisson distribution, simple and multivariate normal distributions</li>
|
||||
<li> Central elements of Bayesian statistics and modeling</li>
|
||||
<li> Gradient methods for data optimization</li>
|
||||
<li> Monte Carlo methods, Markov chains, Metropolis-Hastings algorithm</li>
|
||||
<li> Linear methods for regression and classification</li>
|
||||
<li> Estimation of errors using cross-validation, blocking, bootstrapping and jackknife methods</li>
|
||||
<li> Practical optimization using Singular-value decomposition and least squares for parameterizing data</li>
|
||||
<li> Basic concepts, expectation values, variance, covariance, correlation functions and errors;</li>
|
||||
<li> Simpler models, binomial distribution, the Poisson distribution, simple and multivariate normal distributions;</li>
|
||||
<li> Central elements of Bayesian statistics and modeling;</li>
|
||||
<li> Gradient methods for data optimization,</li>
|
||||
<li> Monte Carlo methods, Markov chains, Gibbs sampling and Metropolis-Hastings sampling;</li>
|
||||
<li> Estimation of errors and resampling techniques such as the cross-validation, blocking, bootstrapping and jackknife methods;</li>
|
||||
<li> Principal Component Analysis (PCA) and its mathematical foundation</li>
|
||||
</ul>
|
||||
</div>
|
||||
|
||||
@@ -288,7 +318,7 @@ Projects are handed in using devilry.ifi.uio.no. We use Github as repository for
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec7">Topics covered in this course: Machine Learning </h2>
|
||||
<h2 id="___sec8">Topics covered in this course: Machine Learning </h2>
|
||||
|
||||
<p>
|
||||
<div class="alert alert-block alert-block alert-text-normal">
|
||||
@@ -297,21 +327,25 @@ Projects are handed in using devilry.ifi.uio.no. We use Github as repository for
|
||||
The following topics will be covered
|
||||
|
||||
<ul>
|
||||
<li> Linear Regression and Logistic Regression</li>
|
||||
<li> Neural networks and deep learning</li>
|
||||
<li> Decisions trees and nearest neighbor algorithms</li>
|
||||
<li> Linear Regression and Logistic Regression;</li>
|
||||
<li> Neural networks and deep learning, including convolutional and recurrent neural networks</li>
|
||||
<li> Decisions trees, Random Forests, Bagging and Boosting</li>
|
||||
<li> Support vector machines</li>
|
||||
<li> Bayesian Neural Networks</li>
|
||||
<li> Bayesian linear and logistic regression</li>
|
||||
<li> Boltzmann Machines</li>
|
||||
<li> Dimensionality reduction, from PCA to cluster models</li>
|
||||
<li> Unsupervised learning Dimensionality reduction, from PCA to cluster models</li>
|
||||
</ul>
|
||||
|
||||
Hands-on demonstrations, exercises and projects aim at deepening your understanding of these topics.
|
||||
|
||||
|
||||
</div>
|
||||
|
||||
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec8">Extremely useful tools, strongly recommended </h2>
|
||||
<h2 id="___sec9">Extremely useful tools, strongly recommended </h2>
|
||||
|
||||
<p>
|
||||
<div class="alert alert-block alert-block alert-text-normal">
|
||||
@@ -319,9 +353,8 @@ The following topics will be covered
|
||||
<p>
|
||||
|
||||
<ul>
|
||||
<li> GIT for version control, highly recommended</li>
|
||||
<li> Devilry for handing in projects, next week</li>
|
||||
<li> Anaconda and other Python environments, see intro slides</li>
|
||||
<li> GIT for version control, and GitHub or GitLab as repositories, highly recommended. This will be discussed during the first exercise session</li>
|
||||
<li> Anaconda and other Python environments, see intro slides and first exercise session</li>
|
||||
</ul>
|
||||
</div>
|
||||
|
||||
@@ -329,7 +362,7 @@ The following topics will be covered
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec9">Other courses on Data science and Machine Learning at UiO </h2>
|
||||
<h2 id="___sec10">Other courses on Data science and Machine Learning at UiO </h2>
|
||||
|
||||
<p>
|
||||
The link here <a href="https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/" target="_blank"><tt>https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/</tt></a> gives an excellent overview of courses on Machine learning at UiO.
|
||||
@@ -353,7 +386,7 @@ The link here <a href="https://www.mn.uio.no/english/research/about/centre-focus
|
||||
|
||||
|
||||
<center style="font-size:80%">
|
||||
<!-- copyright --> © 1999-2019, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
|
||||
<!-- copyright --> © 1999-2020, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
|
||||
</center>
|
||||
|
||||
|
||||
|
||||
@@ -69,26 +69,27 @@ div { text-align: justify; text-justify: inter-word; }
|
||||
'sections': [('Overview of first week', 2, None, '___sec0'),
|
||||
('Lectures and ComputerLab', 2, None, '___sec1'),
|
||||
('Course Format', 2, None, '___sec2'),
|
||||
('Teachers and ComputerLab', 2, None, '___sec3'),
|
||||
('Teachers', 2, None, '___sec3'),
|
||||
('Deadlines for projects (tentative)', 2, None, '___sec4'),
|
||||
('Learning outcomes', 2, None, '___sec5'),
|
||||
('Prerequisites', 2, None, '___sec5'),
|
||||
('Learning outcomes', 2, None, '___sec6'),
|
||||
('Topics covered in this course: Statistical analysis and '
|
||||
'optimization of data',
|
||||
2,
|
||||
None,
|
||||
'___sec6'),
|
||||
'___sec7'),
|
||||
('Topics covered in this course: Machine Learning',
|
||||
2,
|
||||
None,
|
||||
'___sec7'),
|
||||
'___sec8'),
|
||||
('Extremely useful tools, strongly recommended',
|
||||
2,
|
||||
None,
|
||||
'___sec8'),
|
||||
'___sec9'),
|
||||
('Other courses on Data science and Machine Learning at UiO',
|
||||
2,
|
||||
None,
|
||||
'___sec9')]}
|
||||
'___sec10')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -114,7 +115,7 @@ end of tocinfo -->
|
||||
<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
|
||||
<br>
|
||||
<p>
|
||||
<center><h4>Nov 12, 2019</h4></center> <!-- date -->
|
||||
<center><h4>Aug 19, 2020</h4></center> <!-- date -->
|
||||
<br>
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
@@ -127,10 +128,10 @@ end of tocinfo -->
|
||||
<p>
|
||||
|
||||
<ul>
|
||||
<li> Thursday August 22: First lecture: Presentation of the course, aims and content</li>
|
||||
<li> Thursday: Second Lecture: Start with simple linear regression and repetition of linear algebra</li>
|
||||
<li> Friday August 23: Linear regression</li>
|
||||
<li> Computer lab: Tuesday. First time: Tuesday August 27.</li>
|
||||
<li> Thursday August 20: First lecture: Presentation of the course, aims and content</li>
|
||||
<li> Thursday: Second Lecture: Start with simple linear regression and repetition of linear algebra and elements of statistics</li>
|
||||
<li> Friday August 21: Linear regression</li>
|
||||
<li> Computer lab: Wednesdays, 8am-6pm. First time: Wednesday August 26.</li>
|
||||
</ul>
|
||||
</div>
|
||||
|
||||
@@ -146,12 +147,10 @@ end of tocinfo -->
|
||||
<p>
|
||||
|
||||
<ul>
|
||||
<li> Lectures: Thursday (2.15pm-4pm, this may change) and Friday (12.15pm-2pm).</li>
|
||||
<li> Weekly reading assignments needed to solve projects and exercises.</li>
|
||||
<li> Lectures: Thursday (12.15pm-2pm and Friday (12.15pm-2pm). Due to the present COVID-19 situation all lectures will be online. They will be recorded and posted online at the official UiO <a href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/index.html" target="_blank">website</a>.</li>
|
||||
<li> Weekly reading assignments and videos needed to solve projects and exercises.</li>
|
||||
<li> Weekly exercises when not working on projects. You can hand in exercises if you want.</li>
|
||||
<li> First hour of each lab session may be used to discuss technicalities, address questions etc linked with projects and exercises.</li>
|
||||
<li> Detailed lecture notes, exercises, all programs presented, projects etc can be found at the homepage of the course.</li>
|
||||
<li> Computerlab: Tuesday (8am-4pm), VB IT-auditorium 3. Depending on how many enlist we may extend the lab sessions</li>
|
||||
<li> Weekly plans and all other information are on the official webpage.</li>
|
||||
<li> No final exam, three projects that are graded and have to be approved.</li>
|
||||
</ul>
|
||||
@@ -187,7 +186,7 @@ end of tocinfo -->
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec3">Teachers and ComputerLab </h2>
|
||||
<h2 id="___sec3">Teachers </h2>
|
||||
|
||||
<p>
|
||||
<div class="alert alert-block alert-block alert-text-normal">
|
||||
@@ -197,26 +196,26 @@ end of tocinfo -->
|
||||
<p>
|
||||
<b>Teachers :</b>
|
||||
|
||||
<ol>
|
||||
<li> <a href="https://www.researchgate.net/profile/Hanna_Svennevik" target="_blank">Hanna Svennevik</a></li>
|
||||
<li> <a href="http://mhjgit.github.io/info/doc/web/" target="_blank">Morten Hjorth-Jensen</a></li>
|
||||
<li> <a href="https://no.linkedin.com/in/lucas-charpentier-176206171" target="_blank">Lucas Charpentier</a></li>
|
||||
<li> <a href="https://www.researchgate.net/profile/Stian_Bilek" target="_blank">Stian Bilek</a></li>
|
||||
<li> <a href="https://github.com/Schoyen" target="_blank">Øyvind Sigmundson Schøyen</a></li>
|
||||
</ol>
|
||||
<ul>
|
||||
<li> Morten Hjorth-Jensen, morten.hjorth-jensen@fys.uio.no</li>
|
||||
|
||||
<table border="1">
|
||||
<thead>
|
||||
<tr><th align="center"> day </th> <th align="center"> Time </th> </tr>
|
||||
</thead>
|
||||
<tbody>
|
||||
<tr><td align="center"> Group 1: Tuesday </td> <td align="center"> 8am-10am </td> </tr>
|
||||
<tr><td align="center"> Group 2: Tuesday </td> <td align="center"> 10am-12pm </td> </tr>
|
||||
<tr><td align="center"> Group 3: Tuesday </td> <td align="center"> 12pm-2pm </td> </tr>
|
||||
<tr><td align="center"> Group 4: Tuesday </td> <td align="center"> 2pm-4pm </td> </tr>
|
||||
</tbody>
|
||||
</table>
|
||||
<ul>
|
||||
<li> <b>Phone</b>: +47-48257387</li>
|
||||
<li> <b>Office</b>: Department of Physics, University of Oslo, Eastern wing, room FØ470</li>
|
||||
<li> <b>Office hours</b>: <em>Anytime</em>! In Fall Semester 2020 (FS20), as a rule of thumb office hours are planned via computer or telephone. Individual or group office hours will be performed via zoom. Feel free to send an email for planning. In person meetings may also be possible if allowed by the University of Oslo's COVID-19 instructions.</li>
|
||||
</ul>
|
||||
|
||||
<li> Øyvind Sigmundson Schøyen, oyvinssc@student.matnat.uio.no</li>
|
||||
|
||||
<ul>
|
||||
<li> <b>Office</b>: Department of Physics, University of Oslo, Eastern wing, room FØ452</li>
|
||||
</ul>
|
||||
|
||||
<li> Michael Bitney, m.s.bitney@fys.uio.no</li>
|
||||
<li> Kristian Wold, kriswold@student.matnat.uio.no</li>
|
||||
<li> Nicolai Haug, nicoha@student.matnat.uio.no</li>
|
||||
<li> Per-Dimitri Sønsteland, perdimitri.bs@gmail.com</li>
|
||||
</ul>
|
||||
</div>
|
||||
|
||||
|
||||
@@ -231,12 +230,12 @@ end of tocinfo -->
|
||||
<p>
|
||||
|
||||
<ol>
|
||||
<li> Project 1: September 30 (graded with feedback)</li>
|
||||
<li> Project 2: November 13 (graded with feedback)</li>
|
||||
<li> Project 3: December 15 (graded with feedback)</li>
|
||||
<li> Project 1: September 28 (graded with feedback)</li>
|
||||
<li> Project 2: November 2 (graded with feedback)</li>
|
||||
<li> Project 3: December 7 (graded with feedback)</li>
|
||||
</ol>
|
||||
|
||||
Projects are handed in using devilry.ifi.uio.no. We use Github as repository for codes, benchmark calculations etc. Comments and feedback on projects only via devilry.
|
||||
Projects are handed in using <b>Canvas</b>. We use Github as repository for codes, benchmark calculations etc. Comments and feedback on projects only via <b>Canvas</b>.
|
||||
|
||||
|
||||
</div>
|
||||
@@ -245,24 +244,43 @@ Projects are handed in using devilry.ifi.uio.no. We use Github as repository for
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec5">Learning outcomes </h2>
|
||||
<h2 id="___sec5">Prerequisites </h2>
|
||||
|
||||
<p>
|
||||
Basic knowledge in programming and mathematics, with an emphasis on
|
||||
linear algebra. Knowledge of Python or/and C++ as programming
|
||||
languages is strongly recommended and experience with Jupiter notebook
|
||||
is recommended. Required courses are the equivalents to the University
|
||||
of Oslo mathematics courses MAT1100, MAT1110, MAT1120 and at least one
|
||||
of the corresponding computing and programming courses INF1000/INF1110
|
||||
or MAT-INF1100/MAT-INF1100L/BIOS1100/KJM-INF1100. Most universities
|
||||
offer nowadays a basic programming course (often compulsory) where
|
||||
Python is the recurring programming language.
|
||||
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec6">Learning outcomes </h2>
|
||||
|
||||
<p>
|
||||
<div class="alert alert-block alert-block alert-text-normal">
|
||||
<b></b>
|
||||
<p>
|
||||
|
||||
<p>
|
||||
This course aims at giving you insights and knowledge about many of the central algorithms used in Data Analysis and Machine Learning. The course is project based and through various numerical projects, normally three, you will be exposed to fundamental research problems in these fields, with the aim to reproduce state of the art scientific results. Both supervised and unsupervised methods will be covered. The emphasis is on a frequentist approach, although we will try to link it with a Bayesian approach as well. You will learn to develop and structure large codes for studying different cases where Machine Learning is applied to, get acquainted with computing facilities and learn to handle large scientific projects. A good scientific and ethical conduct is emphasized throughout the course. More specifically, after this course you will
|
||||
|
||||
<ul>
|
||||
<li> Learn about basic data analysis, statistical analysis, Bayesian statistics, Monte Carlo sampling, data optimization and machine learning</li>
|
||||
<li> Be capable of extending the acquired knowledge to other systems and cases</li>
|
||||
<li> Have an understanding of central algorithms used in data analysis and machine learning</li>
|
||||
<li> Gain knowledge of central aspects of Monte Carlo methods, Markov chains, Gibbs samplers and their possible applications</li>
|
||||
<li> Understand linear methods for regression and classification, from ordinary least squares, via Lasso and Ridge to Logistic regression</li>
|
||||
<li> Learn about various neural networks and deep learning methods for supervised and unsupervised learning</li>
|
||||
<li> Learn about about decision trees and random forests</li>
|
||||
<li> Learn about support vector machines and kernel transformations</li>
|
||||
<li> Reduction of data sets, from PCA to clustering, supervised and unsupervided methods</li>
|
||||
<li> Work on numerical projects to illustrate the theory. The projects play a central role and you are expected to know modern programming languages like Python or C++</li>
|
||||
<li> Learn about basic data analysis, statistical analysis, Bayesian statistics, Monte Carlo sampling, data optimization and machine learning;</li>
|
||||
<li> Be capable of extending the acquired knowledge to other systems and cases;</li>
|
||||
<li> Have an understanding of central algorithms used in data analysis and machine learning;</li>
|
||||
<li> Understand linear methods for regression and classification, from ordinary least squares, via Lasso and Ridge to Logistic regression;</li>
|
||||
<li> Learn about neural networks and deep learning methods for supervised and unsupervised learning. Emphasis on feed forward neural networks, convolutional and recurrent neural networks;</li>
|
||||
<li> Learn about about decision trees, random forests, bagging and boosting methods;</li>
|
||||
<li> Learn about support vector machines and kernel transformations;</li>
|
||||
<li> Reduction of data sets, from PCA to clustering;</li>
|
||||
<li> Autoencoders and Reinforcement Learning;</li>
|
||||
<li> Work on numerical projects to illustrate the theory. The projects play a central role and you are expected to know modern programming languages like Python or C++ and/or Fortran (Fortran2003 or later).</li>
|
||||
</ul>
|
||||
</div>
|
||||
|
||||
@@ -270,22 +288,34 @@ Projects are handed in using devilry.ifi.uio.no. We use Github as repository for
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec6">Topics covered in this course: Statistical analysis and optimization of data </h2>
|
||||
<h2 id="___sec7">Topics covered in this course: Statistical analysis and optimization of data </h2>
|
||||
|
||||
<p>
|
||||
The course has two central parts
|
||||
|
||||
<ol>
|
||||
<li> Statistical analysis and optimization of data</li>
|
||||
<li> Machine learning</li>
|
||||
</ol>
|
||||
|
||||
These topics will be scattered thorughout the course and may not necessarily be taught separately. Rather, we will often take an approach (during the lectures and project/exercise sessions) where say elements from statistical data analysis are mixed with specific Machine Learning algorithms
|
||||
|
||||
<p>
|
||||
<div class="alert alert-block alert-block alert-text-normal">
|
||||
<b></b>
|
||||
<b>Statistical analysis and optimization of data.</b>
|
||||
<p>
|
||||
|
||||
<p>
|
||||
The following topics will be covered
|
||||
|
||||
<ul>
|
||||
<li> Basic concepts, expectation values, variance, covariance, correlation functions and errors</li>
|
||||
<li> Simpler models, binomial distribution, the Poisson distribution, simple and multivariate normal distributions</li>
|
||||
<li> Central elements of Bayesian statistics and modeling</li>
|
||||
<li> Gradient methods for data optimization</li>
|
||||
<li> Monte Carlo methods, Markov chains, Metropolis-Hastings algorithm</li>
|
||||
<li> Linear methods for regression and classification</li>
|
||||
<li> Estimation of errors using cross-validation, blocking, bootstrapping and jackknife methods</li>
|
||||
<li> Practical optimization using Singular-value decomposition and least squares for parameterizing data</li>
|
||||
<li> Basic concepts, expectation values, variance, covariance, correlation functions and errors;</li>
|
||||
<li> Simpler models, binomial distribution, the Poisson distribution, simple and multivariate normal distributions;</li>
|
||||
<li> Central elements of Bayesian statistics and modeling;</li>
|
||||
<li> Gradient methods for data optimization,</li>
|
||||
<li> Monte Carlo methods, Markov chains, Gibbs sampling and Metropolis-Hastings sampling;</li>
|
||||
<li> Estimation of errors and resampling techniques such as the cross-validation, blocking, bootstrapping and jackknife methods;</li>
|
||||
<li> Principal Component Analysis (PCA) and its mathematical foundation</li>
|
||||
</ul>
|
||||
</div>
|
||||
|
||||
@@ -293,7 +323,7 @@ Projects are handed in using devilry.ifi.uio.no. We use Github as repository for
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec7">Topics covered in this course: Machine Learning </h2>
|
||||
<h2 id="___sec8">Topics covered in this course: Machine Learning </h2>
|
||||
|
||||
<p>
|
||||
<div class="alert alert-block alert-block alert-text-normal">
|
||||
@@ -302,21 +332,25 @@ Projects are handed in using devilry.ifi.uio.no. We use Github as repository for
|
||||
The following topics will be covered
|
||||
|
||||
<ul>
|
||||
<li> Linear Regression and Logistic Regression</li>
|
||||
<li> Neural networks and deep learning</li>
|
||||
<li> Decisions trees and nearest neighbor algorithms</li>
|
||||
<li> Linear Regression and Logistic Regression;</li>
|
||||
<li> Neural networks and deep learning, including convolutional and recurrent neural networks</li>
|
||||
<li> Decisions trees, Random Forests, Bagging and Boosting</li>
|
||||
<li> Support vector machines</li>
|
||||
<li> Bayesian Neural Networks</li>
|
||||
<li> Bayesian linear and logistic regression</li>
|
||||
<li> Boltzmann Machines</li>
|
||||
<li> Dimensionality reduction, from PCA to cluster models</li>
|
||||
<li> Unsupervised learning Dimensionality reduction, from PCA to cluster models</li>
|
||||
</ul>
|
||||
|
||||
Hands-on demonstrations, exercises and projects aim at deepening your understanding of these topics.
|
||||
|
||||
|
||||
</div>
|
||||
|
||||
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec8">Extremely useful tools, strongly recommended </h2>
|
||||
<h2 id="___sec9">Extremely useful tools, strongly recommended </h2>
|
||||
|
||||
<p>
|
||||
<div class="alert alert-block alert-block alert-text-normal">
|
||||
@@ -324,9 +358,8 @@ The following topics will be covered
|
||||
<p>
|
||||
|
||||
<ul>
|
||||
<li> GIT for version control, highly recommended</li>
|
||||
<li> Devilry for handing in projects, next week</li>
|
||||
<li> Anaconda and other Python environments, see intro slides</li>
|
||||
<li> GIT for version control, and GitHub or GitLab as repositories, highly recommended. This will be discussed during the first exercise session</li>
|
||||
<li> Anaconda and other Python environments, see intro slides and first exercise session</li>
|
||||
</ul>
|
||||
</div>
|
||||
|
||||
@@ -334,7 +367,7 @@ The following topics will be covered
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec9">Other courses on Data science and Machine Learning at UiO </h2>
|
||||
<h2 id="___sec10">Other courses on Data science and Machine Learning at UiO </h2>
|
||||
|
||||
<p>
|
||||
The link here <a href="https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/" target="_blank"><tt>https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/</tt></a> gives an excellent overview of courses on Machine learning at UiO.
|
||||
@@ -358,7 +391,7 @@ The link here <a href="https://www.mn.uio.no/english/research/about/centre-focus
|
||||
|
||||
|
||||
<center style="font-size:80%">
|
||||
<!-- copyright --> © 1999-2019, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
|
||||
<!-- copyright --> © 1999-2020, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
|
||||
</center>
|
||||
|
||||
|
||||
|
||||
Binary file not shown.
Binary file not shown.
@@ -8,10 +8,10 @@ DATE: today
|
||||
===== Overview of first week =====
|
||||
|
||||
!bblock
|
||||
* Thursday August 22: First lecture: Presentation of the course, aims and content
|
||||
* Thursday: Second Lecture: Start with simple linear regression and repetition of linear algebra
|
||||
* Friday August 23: Linear regression
|
||||
* Computer lab: Tuesday. First time: Tuesday August 27.
|
||||
* Thursday August 20: First lecture: Presentation of the course, aims and content
|
||||
* Thursday: Second Lecture: Start with simple linear regression and repetition of linear algebra and elements of statistics
|
||||
* Friday August 21: Linear regression
|
||||
* Computer lab: Wednesdays, 8am-6pm. First time: Wednesday August 26.
|
||||
!eblock
|
||||
|
||||
|
||||
@@ -19,12 +19,10 @@ DATE: today
|
||||
===== Lectures and ComputerLab =====
|
||||
|
||||
!bblock
|
||||
* Lectures: Thursday (2.15pm-4pm, this may change) and Friday (12.15pm-2pm).
|
||||
* Weekly reading assignments needed to solve projects and exercises.
|
||||
* Lectures: Thursday (12.15pm-2pm and Friday (12.15pm-2pm). Due to the present COVID-19 situation all lectures will be online. They will be recorded and posted online at the official UiO "website":"https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/index.html".
|
||||
* Weekly reading assignments and videos needed to solve projects and exercises.
|
||||
* Weekly exercises when not working on projects. You can hand in exercises if you want.
|
||||
* First hour of each lab session may be used to discuss technicalities, address questions etc linked with projects and exercises.
|
||||
* Detailed lecture notes, exercises, all programs presented, projects etc can be found at the homepage of the course.
|
||||
* Computerlab: Tuesday (8am-4pm), VB IT-auditorium 3. Depending on how many enlist we may extend the lab sessions
|
||||
* Weekly plans and all other information are on the official webpage.
|
||||
* No final exam, three projects that are graded and have to be approved.
|
||||
!eblock
|
||||
@@ -49,29 +47,24 @@ DATE: today
|
||||
|
||||
|
||||
!split
|
||||
===== Teachers and ComputerLab =====
|
||||
===== Teachers =====
|
||||
|
||||
!bblock
|
||||
|
||||
_Teachers :_
|
||||
* Morten Hjorth-Jensen, morten.hjorth-jensen@fys.uio.no
|
||||
* _Phone_: +47-48257387
|
||||
* _Office_: Department of Physics, University of Oslo, Eastern wing, room FØ470
|
||||
* _Office hours_: *Anytime*! In Fall Semester 2020 (FS20), as a rule of thumb office hours are planned via computer or telephone. Individual or group office hours will be performed via zoom. Feel free to send an email for planning. In person meetings may also be possible if allowed by the University of Oslo's COVID-19 instructions.
|
||||
|
||||
o "Hanna Svennevik":"https://www.researchgate.net/profile/Hanna_Svennevik"
|
||||
o "Morten Hjorth-Jensen":"http://mhjgit.github.io/info/doc/web/"
|
||||
o "Lucas Charpentier":"https://no.linkedin.com/in/lucas-charpentier-176206171"
|
||||
o "Stian Bilek":"https://www.researchgate.net/profile/Stian_Bilek"
|
||||
o "Øyvind Sigmundson Schøyen":"https://github.com/Schoyen"
|
||||
* Øyvind Sigmundson Schøyen, oyvinssc@student.matnat.uio.no
|
||||
* _Office_: Department of Physics, University of Oslo, Eastern wing, room FØ452
|
||||
* Michael Bitney, m.s.bitney@fys.uio.no
|
||||
* Kristian Wold, kriswold@student.matnat.uio.no
|
||||
* Nicolai Haug, nicoha@student.matnat.uio.no
|
||||
* Per-Dimitri Sønsteland, perdimitri.bs@gmail.com
|
||||
|
||||
|
||||
|
||||
|------------------------------------------------------|
|
||||
| day | Time |
|
||||
|----------------------------------------------------|
|
||||
| Group 1: Tuesday | 8am-10am |
|
||||
| Group 2: Tuesday | 10am-12pm |
|
||||
| Group 3: Tuesday | 12pm-2pm |
|
||||
| Group 4: Tuesday | 2pm-4pm |
|
||||
|-------------------------------------------------|
|
||||
|
||||
!eblock
|
||||
|
||||
!split
|
||||
@@ -79,46 +72,74 @@ o "Øyvind Sigmundson Schøyen":"https://github.com/Schoyen"
|
||||
|
||||
!bblock
|
||||
|
||||
o Project 1: September 30 (graded with feedback)
|
||||
o Project 2: November 13 (graded with feedback)
|
||||
o Project 3: December 15 (graded with feedback)
|
||||
o Project 1: September 28 (graded with feedback)
|
||||
o Project 2: November 2 (graded with feedback)
|
||||
o Project 3: December 7 (graded with feedback)
|
||||
|
||||
Projects are handed in using devilry.ifi.uio.no. We use Github as repository for codes, benchmark calculations etc. Comments and feedback on projects only via devilry.
|
||||
Projects are handed in using _Canvas_. We use Github as repository for codes, benchmark calculations etc. Comments and feedback on projects only via _Canvas_.
|
||||
|
||||
!eblock
|
||||
|
||||
|
||||
|
||||
|
||||
!split
|
||||
===== Prerequisites =====
|
||||
|
||||
Basic knowledge in programming and mathematics, with an emphasis on
|
||||
linear algebra. Knowledge of Python or/and C++ as programming
|
||||
languages is strongly recommended and experience with Jupiter notebook
|
||||
is recommended. Required courses are the equivalents to the University
|
||||
of Oslo mathematics courses MAT1100, MAT1110, MAT1120 and at least one
|
||||
of the corresponding computing and programming courses INF1000/INF1110
|
||||
or MAT-INF1100/MAT-INF1100L/BIOS1100/KJM-INF1100. Most universities
|
||||
offer nowadays a basic programming course (often compulsory) where
|
||||
Python is the recurring programming language.
|
||||
|
||||
|
||||
|
||||
!split
|
||||
===== Learning outcomes =====
|
||||
|
||||
!bblock
|
||||
|
||||
* Learn about basic data analysis, statistical analysis, Bayesian statistics, Monte Carlo sampling, data optimization and machine learning
|
||||
* Be capable of extending the acquired knowledge to other systems and cases
|
||||
* Have an understanding of central algorithms used in data analysis and machine learning
|
||||
* Gain knowledge of central aspects of Monte Carlo methods, Markov chains, Gibbs samplers and their possible applications
|
||||
* Understand linear methods for regression and classification, from ordinary least squares, via Lasso and Ridge to Logistic regression
|
||||
* Learn about various neural networks and deep learning methods for supervised and unsupervised learning
|
||||
* Learn about about decision trees and random forests
|
||||
* Learn about support vector machines and kernel transformations
|
||||
* Reduction of data sets, from PCA to clustering, supervised and unsupervided methods
|
||||
* Work on numerical projects to illustrate the theory. The projects play a central role and you are expected to know modern programming languages like Python or C++
|
||||
|
||||
This course aims at giving you insights and knowledge about many of the central algorithms used in Data Analysis and Machine Learning. The course is project based and through various numerical projects, normally three, you will be exposed to fundamental research problems in these fields, with the aim to reproduce state of the art scientific results. Both supervised and unsupervised methods will be covered. The emphasis is on a frequentist approach, although we will try to link it with a Bayesian approach as well. You will learn to develop and structure large codes for studying different cases where Machine Learning is applied to, get acquainted with computing facilities and learn to handle large scientific projects. A good scientific and ethical conduct is emphasized throughout the course. More specifically, after this course you will
|
||||
|
||||
* Learn about basic data analysis, statistical analysis, Bayesian statistics, Monte Carlo sampling, data optimization and machine learning;
|
||||
* Be capable of extending the acquired knowledge to other systems and cases;
|
||||
* Have an understanding of central algorithms used in data analysis and machine learning;
|
||||
* Understand linear methods for regression and classification, from ordinary least squares, via Lasso and Ridge to Logistic regression;
|
||||
* Learn about neural networks and deep learning methods for supervised and unsupervised learning. Emphasis on feed forward neural networks, convolutional and recurrent neural networks;
|
||||
* Learn about about decision trees, random forests, bagging and boosting methods;
|
||||
* Learn about support vector machines and kernel transformations;
|
||||
* Reduction of data sets, from PCA to clustering;
|
||||
* Autoencoders and Reinforcement Learning;
|
||||
* Work on numerical projects to illustrate the theory. The projects play a central role and you are expected to know modern programming languages like Python or C++ and/or Fortran (Fortran2003 or later).
|
||||
|
||||
!eblock
|
||||
|
||||
!split
|
||||
===== Topics covered in this course: Statistical analysis and optimization of data =====
|
||||
|
||||
!bblock
|
||||
* Basic concepts, expectation values, variance, covariance, correlation functions and errors
|
||||
* Simpler models, binomial distribution, the Poisson distribution, simple and multivariate normal distributions
|
||||
* Central elements of Bayesian statistics and modeling
|
||||
* Gradient methods for data optimization
|
||||
* Monte Carlo methods, Markov chains, Metropolis-Hastings algorithm
|
||||
* Linear methods for regression and classification
|
||||
* Estimation of errors using cross-validation, blocking, bootstrapping and jackknife methods
|
||||
* Practical optimization using Singular-value decomposition and least squares for parameterizing data
|
||||
The course has two central parts
|
||||
|
||||
o Statistical analysis and optimization of data
|
||||
o Machine learning
|
||||
|
||||
These topics will be scattered thorughout the course and may not necessarily be taught separately. Rather, we will often take an approach (during the lectures and project/exercise sessions) where say elements from statistical data analysis are mixed with specific Machine Learning algorithms
|
||||
|
||||
!bblock Statistical analysis and optimization of data
|
||||
|
||||
The following topics will be covered
|
||||
* Basic concepts, expectation values, variance, covariance, correlation functions and errors;
|
||||
* Simpler models, binomial distribution, the Poisson distribution, simple and multivariate normal distributions;
|
||||
* Central elements of Bayesian statistics and modeling;
|
||||
* Gradient methods for data optimization,
|
||||
* Monte Carlo methods, Markov chains, Gibbs sampling and Metropolis-Hastings sampling;
|
||||
* Estimation of errors and resampling techniques such as the cross-validation, blocking, bootstrapping and jackknife methods;
|
||||
* Principal Component Analysis (PCA) and its mathematical foundation
|
||||
|
||||
!eblock
|
||||
|
||||
|
||||
@@ -127,13 +148,16 @@ Projects are handed in using devilry.ifi.uio.no. We use Github as repository for
|
||||
|
||||
!bblock
|
||||
The following topics will be covered
|
||||
* Linear Regression and Logistic Regression
|
||||
* Neural networks and deep learning
|
||||
* Decisions trees and nearest neighbor algorithms
|
||||
* Linear Regression and Logistic Regression;
|
||||
* Neural networks and deep learning, including convolutional and recurrent neural networks
|
||||
* Decisions trees, Random Forests, Bagging and Boosting
|
||||
* Support vector machines
|
||||
* Bayesian Neural Networks
|
||||
* Bayesian linear and logistic regression
|
||||
* Boltzmann Machines
|
||||
* Dimensionality reduction, from PCA to cluster models
|
||||
* Unsupervised learning Dimensionality reduction, from PCA to cluster models
|
||||
|
||||
Hands-on demonstrations, exercises and projects aim at deepening your understanding of these topics.
|
||||
|
||||
!eblock
|
||||
|
||||
|
||||
@@ -141,9 +165,8 @@ The following topics will be covered
|
||||
===== Extremely useful tools, strongly recommended =====
|
||||
|
||||
!bblock and discussed at the lab sessions
|
||||
* GIT for version control, highly recommended
|
||||
* Devilry for handing in projects, next week
|
||||
* Anaconda and other Python environments, see intro slides
|
||||
* GIT for version control, and GitHub or GitLab as repositories, highly recommended. This will be discussed during the first exercise session
|
||||
* Anaconda and other Python environments, see intro slides and first exercise session
|
||||
!eblock
|
||||
|
||||
|
||||
|
||||
@@ -47,7 +47,7 @@ system doconce format html $name --html_style=bootstrap --pygments_html_style=de
|
||||
system doconce split_html $html.html --method=split --pagination --nav_button=bottom
|
||||
|
||||
# IPython notebook
|
||||
system doconce format ipynb $name $opt
|
||||
#system doconce format ipynb $name $opt
|
||||
|
||||
|
||||
# Ordinary plain LaTeX document
|
||||
|
||||
@@ -0,0 +1,123 @@
|
||||
import tensorflow.compat.v1 as tf
|
||||
tf.disable_v2_behavior()
|
||||
tf.reset_default_graph()
|
||||
|
||||
# import necessary packages
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from sklearn import datasets
|
||||
|
||||
|
||||
# ensure the same random numbers appear every time
|
||||
np.random.seed(0)
|
||||
|
||||
plt.rcParams['figure.figsize'] = (12,12)
|
||||
|
||||
|
||||
# download MNIST dataset
|
||||
digits = datasets.load_digits()
|
||||
|
||||
# define inputs and labels
|
||||
inputs = digits.images
|
||||
labels = digits.target
|
||||
|
||||
print("inputs = (n_inputs, pixel_width, pixel_height) = " + str(inputs.shape))
|
||||
print("labels = (n_inputs) = " + str(labels.shape))
|
||||
|
||||
|
||||
# flatten the image
|
||||
# the value -1 means dimension is inferred from the remaining dimensions: 8x8 = 64
|
||||
n_inputs = len(inputs)
|
||||
inputs = inputs.reshape(n_inputs, -1)
|
||||
print("X = (n_inputs, n_features) = " + str(inputs.shape))
|
||||
|
||||
|
||||
# choose some random images to display
|
||||
indices = np.arange(n_inputs)
|
||||
random_indices = np.random.choice(indices, size=5)
|
||||
|
||||
for i, image in enumerate(digits.images[random_indices]):
|
||||
plt.subplot(1, 5, i+1)
|
||||
plt.axis('off')
|
||||
plt.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest')
|
||||
plt.title("Label: %d" % digits.target[random_indices[i]])
|
||||
plt.show()
|
||||
|
||||
from keras.utils import to_categorical
|
||||
from sklearn.model_selection import train_test_split
|
||||
|
||||
# one-hot representation of labels
|
||||
labels = to_categorical(labels)
|
||||
|
||||
# split into train and test data
|
||||
train_size = 0.8
|
||||
test_size = 1 - train_size
|
||||
X_train, X_test, Y_train, Y_test = train_test_split(inputs, labels, train_size=train_size,
|
||||
test_size=test_size)
|
||||
|
||||
epochs = 100
|
||||
batch_size = 100
|
||||
n_neurons_layer1 = 100
|
||||
n_neurons_layer2 = 50
|
||||
n_categories = 10
|
||||
eta_vals = np.logspace(-5, 1, 7)
|
||||
lmbd_vals = np.logspace(-5, 1, 7)
|
||||
from keras.models import Sequential
|
||||
from keras.layers import Dense
|
||||
from keras.regularizers import l2
|
||||
from keras.optimizers import SGD
|
||||
|
||||
def create_neural_network_keras(n_neurons_layer1, n_neurons_layer2, n_categories, eta, lmbd):
|
||||
model = Sequential()
|
||||
model.add(Dense(n_neurons_layer1, activation='sigmoid', kernel_regularizer=l2(lmbd)))
|
||||
model.add(Dense(n_neurons_layer2, activation='sigmoid', kernel_regularizer=l2(lmbd)))
|
||||
model.add(Dense(n_categories, activation='softmax'))
|
||||
|
||||
sgd = SGD(lr=eta)
|
||||
model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
|
||||
|
||||
return model
|
||||
|
||||
DNN_keras = np.zeros((len(eta_vals), len(lmbd_vals)), dtype=object)
|
||||
|
||||
for i, eta in enumerate(eta_vals):
|
||||
for j, lmbd in enumerate(lmbd_vals):
|
||||
DNN = create_neural_network_keras(n_neurons_layer1, n_neurons_layer2, n_categories,eta=eta, lmbd=lmbd)
|
||||
DNN.fit(X_train, Y_train, epochs=epochs, batch_size=batch_size, verbose=0)
|
||||
scores = DNN.evaluate(X_test, Y_test)
|
||||
|
||||
DNN_keras[i][j] = DNN
|
||||
|
||||
print("Learning rate = ", eta)
|
||||
print("Lambda = ", lmbd)
|
||||
print("Test accuracy: %.3f" % scores[1])
|
||||
print()
|
||||
|
||||
import seaborn as sns
|
||||
|
||||
sns.set()
|
||||
|
||||
train_accuracy = np.zeros((len(eta_vals), len(lmbd_vals)))
|
||||
test_accuracy = np.zeros((len(eta_vals), len(lmbd_vals)))
|
||||
|
||||
for i in range(len(eta_vals)):
|
||||
for j in range(len(lmbd_vals)):
|
||||
DNN = DNN_keras[i][j]
|
||||
|
||||
train_accuracy[i][j] = DNN.evaluate(X_train, Y_train)[1]
|
||||
test_accuracy[i][j] = DNN.evaluate(X_test, Y_test)[1]
|
||||
|
||||
|
||||
fig, ax = plt.subplots(figsize = (10, 10))
|
||||
sns.heatmap(train_accuracy, annot=True, ax=ax, cmap="viridis")
|
||||
ax.set_title("Training Accuracy")
|
||||
ax.set_ylabel("$\eta$")
|
||||
ax.set_xlabel("$\lambda$")
|
||||
plt.show()
|
||||
|
||||
fig, ax = plt.subplots(figsize = (10, 10))
|
||||
sns.heatmap(test_accuracy, annot=True, ax=ax, cmap="viridis")
|
||||
ax.set_title("Test Accuracy")
|
||||
ax.set_ylabel("$\eta$")
|
||||
ax.set_xlabel("$\lambda$")
|
||||
plt.show()
|
||||
Reference in New Issue
Block a user