mhjensen
281 lines
15 KiB
HTML
281 lines
15 KiB
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'sections': [('Overview of first week', 2, None, '___sec0'),
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<a class="navbar-brand" href="week34-bs.html">Week 34: Introduction to the course, Logistics and Practicalities</a>
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<a href="#" class="dropdown-toggle" data-toggle="dropdown">Contents <b class="caret"></b></a>
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<ul class="dropdown-menu">
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<!-- navigation toc: --> <li><a href="._week34-bs001.html#___sec0" style="font-size: 80%;"><b>Overview of first week</b></a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs002.html#___sec1" style="font-size: 80%;"><b>Thursday August 20</b></a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs003.html#___sec2" style="font-size: 80%;"><b>Lectures and ComputerLab</b></a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs004.html#___sec3" style="font-size: 80%;"><b>Course Format</b></a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs005.html#___sec4" style="font-size: 80%;"><b>Teachers</b></a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs006.html#___sec5" style="font-size: 80%;"><b>Deadlines for projects (tentative)</b></a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs007.html#___sec6" style="font-size: 80%;"><b>Recommended textbooks</b></a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs008.html#___sec7" style="font-size: 80%;"><b>Prerequisites</b></a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs009.html#___sec8" style="font-size: 80%;"><b>Learning outcomes</b></a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs010.html#___sec9" style="font-size: 80%;"><b>Topics covered in this course: Statistical analysis and optimization of data</b></a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs011.html#___sec10" style="font-size: 80%;"><b>Topics covered in this course: Machine Learning</b></a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs012.html#___sec11" style="font-size: 80%;"><b>Extremely useful tools, strongly recommended</b></a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs013.html#___sec12" style="font-size: 80%;"><b>Other courses on Data science and Machine Learning at UiO</b></a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs014.html#___sec13" style="font-size: 80%;"><b>Introduction</b></a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs015.html#___sec14" style="font-size: 80%;"><b>What is Machine Learning?</b></a></li>
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<!-- navigation toc: --> <li><a href="#___sec15" style="font-size: 80%;"><b>Types of Machine Learning</b></a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs017.html#___sec16" style="font-size: 80%;"><b>Software and needed installations</b></a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs018.html#___sec17" style="font-size: 80%;"><b>Python installers</b></a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs019.html#___sec18" style="font-size: 80%;"><b>Useful Python libraries</b></a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs020.html#___sec19" style="font-size: 80%;"><b>Installing R, C++, cython or Julia</b></a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs021.html#___sec20" style="font-size: 80%;"><b>Installing R, C++, cython, Numba etc</b></a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs022.html#___sec21" style="font-size: 80%;"><b>Numpy examples and Important Matrix and vector handling packages</b></a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs023.html#___sec22" style="font-size: 80%;"><b>Basic Matrix Features</b></a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs024.html#___sec23" style="font-size: 80%;"> Some famous Matrices</a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs025.html#___sec24" style="font-size: 80%;"> More Basic Matrix Features</a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs026.html#___sec25" style="font-size: 80%;"><b>Numpy and arrays</b></a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs027.html#___sec26" style="font-size: 80%;"><b>Matrices in Python</b></a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs028.html#___sec27" style="font-size: 80%;"><b>Meet the Pandas</b></a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs029.html#___sec28" style="font-size: 80%;"><b>Friday August 21</b></a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs030.html#___sec29" style="font-size: 80%;"><b>Reading Data and fitting</b></a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs031.html#___sec30" style="font-size: 80%;"><b>Friday August 21</b></a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs032.html#___sec31" style="font-size: 80%;"> Simple linear regression model using <b>scikit-learn</b></a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs032.html#___sec32" style="font-size: 80%;"> To our real data: nuclear binding energies. Brief reminder on masses and binding energies</a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs032.html#___sec33" style="font-size: 80%;"> Organizing our data</a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs032.html#___sec34" style="font-size: 80%;"> Seeing the wood for the trees</a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs032.html#___sec35" style="font-size: 80%;"> And what about using neural networks?</a></li>
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<!-- navigation toc: --> <li><a href="._week34-bs032.html#___sec36" style="font-size: 80%;"><b>A first summary</b></a></li>
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</li>
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<p> </p><p> </p><p> </p> <!-- add vertical space -->
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<a name="part0016"></a>
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<!-- !split -->
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<h2 id="___sec15" class="anchor">Types of Machine Learning </h2>
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<p>
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The approaches to machine learning are many, but are often split into
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two main categories. In <em>supervised learning</em> we know the answer to a
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problem, and let the computer deduce the logic behind it. On the other
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hand, <em>unsupervised learning</em> is a method for finding patterns and
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relationship in data sets without any prior knowledge of the system.
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Some authours also operate with a third category, namely
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<em>reinforcement learning</em>. This is a paradigm of learning inspired by
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behavioral psychology, where learning is achieved by trial-and-error,
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solely from rewards and punishment.
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<p>
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Another way to categorize machine learning tasks is to consider the
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desired output of a system. Some of the most common tasks are:
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<ul>
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<li> Classification: Outputs are divided into two or more classes. The goal is to produce a model that assigns inputs into one of these classes. An example is to identify digits based on pictures of hand-written ones. Classification is typically supervised learning.</li>
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<li> Regression: Finding a functional relationship between an input data set and a reference data set. The goal is to construct a function that maps input data to continuous output values.</li>
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<li> Clustering: Data are divided into groups with certain common traits, without knowing the different groups beforehand. It is thus a form of unsupervised learning.</li>
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</ul>
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The methods we cover have three main topics in common, irrespective of
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whether we deal with supervised or unsupervised learning. The first
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ingredient is normally our data set (which can be subdivided into
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training and test data), the second item is a model which is normally a
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function of some parameters. The model reflects our knowledge of the system (or lack thereof). As an example, if we know that our data show a behavior similar to what would be predicted by a polynomial, fitting our data to a polynomial of some degree would then determin our model.
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<p>
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The last ingredient is a so-called <b>cost</b>
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function which allows us to present an estimate on how good our model
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is in reproducing the data it is supposed to train.
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At the heart of basically all ML algorithms there are so-called minimization algorithms, often we end up with various variants of <b>gradient</b> methods.
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<p>
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<p>
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