Week 34: Introduction to the course, Logistics and Practicalities
  • Contents
    • Overview of first week
    • Schedule first week
    • Lectures and ComputerLab
    • Communication channels
    • Course Format
    • Teachers
    • Deadlines for projects (tentative)
    • Grading
    • Reading material
    • Main textbooks
    • Other popular texts
    • Reading suggestions week 34
    • Prerequisites
    • Topics covered in this course: Statistical analysis and optimization of data
    • Statistical analysis and optimization of data
    • Machine Learning
    • Deep learning methods
    • Extremely useful tools, strongly recommended
    • Other courses on Data science and Machine Learning at UiO
    • Other courses on Data science and Machine Learning at UiO, contn
    • Learning outcomes
    • Types of Machine Learning
    • Essential elements of ML
    • An optimization/minimization problem
    • The plethora of machine learning algorithms/methods
    • What Is Generative Modeling?
    • Example of generative modeling, "taken from Generative Deep Learning by David Foster":"https://www.oreilly.com/library/view/generative-deep-learning/9781098134174/ch01.html"
    • Generative Versus Discriminative Modeling
    • Example of discriminative modeling, "taken from Generative Deep Learning by David Foster":"https://www.oreilly.com/library/view/generative-deep-learning/9781098134174/ch01.html"
    • Discriminative Modeling
    • A Frequentist approach to data analysis
    • What is a good model?
    • What is a good model? Can we define it?
    • Software and needed installations
    • Python installers
    • Useful Python libraries
    • Installing R, C++, cython or Julia
    • Installing R, C++, cython, Numba etc
    • Numpy examples and Important Matrix and vector handling packages
    • Numpy and arrays
    • Matrices in Python
    • Meet the Pandas
    • Pandas AI
    •    Simple linear regression model using scikit-learn
    •    To our real data: nuclear binding energies. Brief reminder on masses and binding energies
    •    Organizing our data
    •    And what about using neural networks?
    • A first summary
    • Why Linear Regression (aka Ordinary Least Squares and family)
    • Regression analysis, overarching aims
    • Regression analysis, overarching aims II
    • Examples
    • General linear models and linear algebra
    • Rewriting the fitting procedure as a linear algebra problem
    • Rewriting the fitting procedure as a linear algebra problem, more details
    • Generalizing the fitting procedure as a linear algebra problem
    • Generalizing the fitting procedure as a linear algebra problem
    • Optimizing our parameters
    • Our model for the nuclear binding energies
    • Optimizing our parameters, more details
    • Interpretations and optimizing our parameters
    • Interpretations and optimizing our parameters
    • Interpretations and optimizing our parameters
    • Own code for Ordinary Least Squares
    • Adding error analysis and training set up
    • The \( \chi^2 \) function
    • The \( \chi^2 \) function
    • The \( \chi^2 \) function
    • The \( \chi^2 \) function
    • The \( \chi^2 \) function
    • The \( \chi^2 \) function

 

 

 

Other popular texts

  • Christopher M. Bishop (CB), Pattern Recognition and Machine Learning
  • Hastie, Tibshirani, and Friedman (HTF), The Elements of Statistical Learning, Springer.
  • Aurelien Geron (AG), Hands‑On Machine Learning with Scikit‑Learn and TensorFlow, O'Reilly. This text is very useful since it contains many code examples and hands-on applications of all algorithms discussed in this course.
  • Kevin Murphy (KM), Probabilistic Machine Learning, an Introduction
  • David Foster (DF), Generative Deep Learning, https://www.oreilly.com/library/view/generative-deep-learning/9781098134174/
  • Babcock and Gavras (BG), Generative AI with Python and TensorFlow, https://github.com/PacktPublishing/Hands-On-Generative-AI-with-Python-and-TensorFlow-2

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