Morten Hjorth-Jensen
565 lines
24 KiB
HTML
565 lines
24 KiB
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<h1 class="site-logo" id="site-title">Applied Data Analysis and Machine Learning</h1>
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Applied Data Analysis and Machine Learning
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About the course
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Teachers and Grading
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Textbooks
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Review of Statistics with Resampling Techniques and Linear Algebra
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</p>
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<li class="toctree-l1">
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1. Elements of Probability Theory and Statistical Data Analysis
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</a>
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</li>
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<li class="toctree-l1">
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<a class="reference internal" href="linalg.html">
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2. Linear Algebra, Handling of Arrays and more Python Features
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</a>
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</li>
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</ul>
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<p aria-level="2" class="caption" role="heading">
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<span class="caption-text">
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From Regression to Support Vector Machines
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</span>
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<li class="toctree-l1">
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3. Linear Regression
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</a>
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</li>
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<li class="toctree-l1">
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<a class="reference internal" href="chapter2.html">
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4. Ridge and Lasso Regression
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</a>
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</li>
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<li class="toctree-l1">
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<a class="reference internal" href="chapter3.html">
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5. Resampling Methods
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</a>
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</li>
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<li class="toctree-l1">
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<a class="reference internal" href="chapter4.html">
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6. Logistic Regression
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</a>
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</li>
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<li class="toctree-l1">
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<a class="reference internal" href="chapteroptimization.html">
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7. Optimization, the central part of any Machine Learning algortithm
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</a>
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</li>
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<li class="toctree-l1">
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<a class="reference internal" href="chapter5.html">
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8. Support Vector Machines, overarching aims
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</a>
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</li>
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</ul>
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<p aria-level="2" class="caption" role="heading">
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<span class="caption-text">
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Decision Trees, Ensemble Methods and Boosting
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</span>
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9. Decision trees, overarching aims
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</a>
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<li class="toctree-l1">
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<a class="reference internal" href="chapter7.html">
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10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods
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</a>
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</li>
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</ul>
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<p aria-level="2" class="caption" role="heading">
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<span class="caption-text">
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Dimensionality Reduction
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11. Basic ideas of the Principal Component Analysis (PCA)
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</a>
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</li>
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<li class="toctree-l1">
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12. Clustering and Unsupervised Learning
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</a>
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<p aria-level="2" class="caption" role="heading">
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<span class="caption-text">
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Deep Learning Methods
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13. Neural networks
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</a>
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<li class="toctree-l1">
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<a class="reference internal" href="chapter10.html">
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14. Building a Feed Forward Neural Network
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</a>
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</li>
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<li class="toctree-l1">
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<a class="reference internal" href="chapter11.html">
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15. Solving Differential Equations with Deep Learning
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<li class="toctree-l1">
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<a class="reference internal" href="chapter12.html">
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16. Convolutional Neural Networks
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<a class="reference internal" href="chapter13.html">
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17. Recurrent neural networks: Overarching view
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</a>
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</li>
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</ul>
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<p aria-level="2" class="caption" role="heading">
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<span class="caption-text">
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Weekly material, notes and exercises
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Exercises week 34
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<a class="reference internal" href="week34.html">
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Week 34: Introduction to the course, Logistics and Practicalities
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Exercises week 35
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<a class="reference internal" href="week35.html">
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Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression
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Exercises week 36
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Week 36: Linear Regression and Statistical interpretations
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<a class="reference internal" href="exercisesweek37.html">
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Exercises week 37
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Week 37: Statistical interpretations and Resampling Methods
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<a class="reference internal" href="exercisesweek38.html">
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Exercises week 38
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Week 38: Logistic Regression and Optimization
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Exercises week 39
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Week 39: Optimization and Gradient Methods
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<a class="reference internal" href="week40.html">
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Week 40: Gradient descent methods (continued) and start Neural networks
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Exercises week 41
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Week 41 Neural networks and constructing a neural network code
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Projects
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Project 1 on Machine Learning, deadline October 7 (midnight), 2024
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</a>
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</li>
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<li class="toctree-l1">
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<a class="reference internal" href="project2.html">
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Project 2 on Machine Learning, deadline November 4 (Midnight)
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Textbooks
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Links to relevant courses at the University of Oslo
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<h1>Textbooks</h1>
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<div class="section" id="textbooks">
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<h1>Textbooks<a class="headerlink" href="#textbooks" title="Permalink to this headline">¶</a></h1>
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<p><em>Recommended textbooks</em>:
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The lecture notes are collected as a jupyter-book at <a class="reference external" href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.html">https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.html</a>. In addition to the lecture notes, we recommend the books of Bishop, Murphy and Goodfellow et al. We will follow these texts closely and the weekly reading assignments refer to these two texts.</p>
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<li><p>Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, <a class="reference external" href="https://www.springer.com/gp/book/9780387310732">https://www.springer.com/gp/book/9780387310732</a>. This is the main textbook and this course covers chapters 1-7, 11 and 12. You can download for free the textbook in PDF format at <a class="reference external" href="https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf">https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf</a></p></li>
|
||
<li><p>Ian Goodfellow, Yoshua Bengio, and Aaron Courville. The different chapters are available for free at <a class="reference external" href="https://www.deeplearningbook.org/">https://www.deeplearningbook.org/</a>. Chapters 2-14 are highly recommended. The lectures follow to a large extent this text.</p></li>
|
||
<li><p>Kevin Murphy, Probabilistic Machine Learning, an Introduction, <a class="reference external" href="https://probml.github.io/pml-book/book1.html">https://probml.github.io/pml-book/book1.html</a></p></li>
|
||
</ul>
|
||
<p>The weekly plans will include reading suggestions from the above textbooks.
|
||
<em>Additional textbooks</em>:</p>
|
||
<ul class="simple">
|
||
<li><p>Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, The Elements of Statistical Learning, Springer, <a class="reference external" href="https://www.springer.com/gp/book/9780387848570">https://www.springer.com/gp/book/9780387848570</a>. This is a well-known text and serves as additional literature.</p></li>
|
||
<li><p>Aurelien Geron, Hands‑On Machine Learning with Scikit‑Learn and TensorFlow, O’Reilly, <a class="reference external" href="https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/">https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/</a>. This text is very useful since it contains many code examples and hands-on applications of all algorithms discussed in this course.</p></li>
|
||
</ul>
|
||
<p><em>General learning book on statistical analysis</em>:</p>
|
||
<ul class="simple">
|
||
<li><p>Christian Robert and George Casella, Monte Carlo Statistical Methods, Springer</p></li>
|
||
<li><p>Peter Hoff, A first course in Bayesian statistical models, Springer</p></li>
|
||
</ul>
|
||
<p><em>General Machine Learning Books</em>:</p>
|
||
<ul class="simple">
|
||
<li><p>Kevin Murphy, Machine Learning: A Probabilistic Perspective, MIT Press</p></li>
|
||
<li><p>David J.C. MacKay, Information Theory, Inference, and Learning Algorithms, Cambridge University Press</p></li>
|
||
<li><p>David Barber, Bayesian Reasoning and Machine Learning, Cambridge University Press</p></li>
|
||
</ul>
|
||
</div>
|
||
<div class="section" id="links-to-relevant-courses-at-the-university-of-oslo">
|
||
<h1>Links to relevant courses at the University of Oslo<a class="headerlink" href="#links-to-relevant-courses-at-the-university-of-oslo" title="Permalink to this headline">¶</a></h1>
|
||
<ul class="simple">
|
||
<li><p><em>FYS5429 Advanced Machine Learning for the Physical Sciences</em> <a class="reference external" href="https://www.uio.no/studier/emner/matnat/fys/FYS5429/index-eng.html">https://www.uio.no/studier/emner/matnat/fys/FYS5429/index-eng.html</a></p></li>
|
||
<li><p><em>FYS5419 Quantum Computing and Quantum Machine Learning</em> <a class="reference external" href="https://www.uio.no/studier/emner/matnat/fys/FYS5419/index-eng.html">https://www.uio.no/studier/emner/matnat/fys/FYS5419/index-eng.html</a></p></li>
|
||
<li><p><em>STK2100 Machine learning and statistical methods for prediction and classification</em> <a class="reference external" href="http://www.uio.no/studier/emner/matnat/math/STK2100/index-eng.html">http://www.uio.no/studier/emner/matnat/math/STK2100/index-eng.html</a>.</p></li>
|
||
<li><p><em>IN3050/4050 Introduction to Artificial Intelligence and Machine Learning</em> <a class="reference external" href="https://www.uio.no/studier/emner/matnat/ifi/IN3050/index-eng.html">https://www.uio.no/studier/emner/matnat/ifi/IN3050/index-eng.html</a>. Introductory course in machine learning and AI with an algorithmic approach.</p></li>
|
||
<li><p><em>IN4080 Natural Language Processing</em> <a class="reference external" href="https://www.uio.no/studier/emner/matnat/ifi/IN4080/index.html">https://www.uio.no/studier/emner/matnat/ifi/IN4080/index.html</a>. Probabilistic and machine learning techniques applied to natural language processing.</p></li>
|
||
<li><p><em>IN5550 – Neural Methods in Natural Language Processing</em> <a class="reference external" href="https://www.uio.no/studier/emner/matnat/ifi/IN5550/index.html">https://www.uio.no/studier/emner/matnat/ifi/IN5550/index.html</a>. This course studies a selection of advanced techniques in Natural Language Processing (NLP), with particular emphasis on recent and current research literature. The focus will be on machine learning and specifically deep neural network approaches to the automated analysis of natural language text.</p></li>
|
||
<li><p><em>STK-IN4300 Statistical learning methods in Data Science</em> <a class="reference external" href="https://www.uio.no/studier/emner/matnat/math/STK-IN4300/index-eng.html">https://www.uio.no/studier/emner/matnat/math/STK-IN4300/index-eng.html</a>. An advanced introduction to statistical and machine learning. For students with a good mathematics and statistics background.</p></li>
|
||
<li><p><em>IN-STK5000 Adaptive Methods for Data-Based Decision Making</em> <a class="reference external" href="https://www.uio.no/studier/emner/matnat/ifi/IN-STK5000/index-eng.html">https://www.uio.no/studier/emner/matnat/ifi/IN-STK5000/index-eng.html</a>. Methods for adaptive collection and processing of data based on machine learning techniques.</p></li>
|
||
<li><p><em>IN4310 Deep Learning for Image Analysis</em> <a class="reference external" href="https://www.uio.no/studier/emner/matnat/ifi/IN4310/index.html">https://www.uio.no/studier/emner/matnat/ifi/IN4310/index.html</a>. An introduction to deep learning with particular emphasis on applications within Image analysis, but useful for other application areas too.</p></li>
|
||
<li><p><em>STK4051 Computational Statistics</em> <a class="reference external" href="https://www.uio.no/studier/emner/matnat/math/STK4051/index-eng.html">https://www.uio.no/studier/emner/matnat/math/STK4051/index-eng.html</a></p></li>
|
||
<li><p><em>STK4021 Applied Bayesian Analysis and Numerical Methods</em> <a class="reference external" href="https://www.uio.no/studier/emner/matnat/math/STK4021/">https://www.uio.no/studier/emner/matnat/math/STK4021/</a></p></li>
|
||
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