mhjensen
166 lines
8.2 KiB
Plaintext
166 lines
8.2 KiB
Plaintext
{
|
||
"cells": [
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"<!-- dom:TITLE: Summary of course -->\n",
|
||
"# Summary of course\n",
|
||
"<!-- dom:AUTHOR: Morten Hjorth-Jensen Email morten.hjorth-jensen@fys.uio.no at Department of Physics and Center of Mathematics for Applications, University of Oslo & National Superconducting Cyclotron Laboratory, Michigan State University -->\n",
|
||
"<!-- Author: --> \n",
|
||
"**Morten Hjorth-Jensen Email morten.hjorth-jensen@fys.uio.no**, Department of Physics and Center of Mathematics for Applications, University of Oslo and National Superconducting Cyclotron Laboratory, Michigan State University\n",
|
||
"\n",
|
||
"Date: **Nov 29, 2018**\n",
|
||
"\n",
|
||
"Copyright 1999-2018, Morten Hjorth-Jensen Email morten.hjorth-jensen@fys.uio.no. Released under CC Attribution-NonCommercial 4.0 license\n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"## What? Me worry? No final exam in this course!\n",
|
||
"<!-- dom:FIGURE: [figures/exam1.jpeg, width=500 frac=0.6] -->\n",
|
||
"<!-- begin figure -->\n",
|
||
"\n",
|
||
"<p></p>\n",
|
||
"<img src=\"figures/exam1.jpeg\" width=500>\n",
|
||
"\n",
|
||
"<!-- end figure -->\n",
|
||
"\n",
|
||
"<!-- dom:FIGURE: [figures/whatmeworry.jpeg, width=500 frac=0.6] -->\n",
|
||
"<!-- begin figure -->\n",
|
||
"\n",
|
||
"<p></p>\n",
|
||
"<img src=\"figures/whatmeworry.jpeg\" width=500>\n",
|
||
"\n",
|
||
"<!-- end figure -->\n",
|
||
"\n",
|
||
"\n",
|
||
"\n",
|
||
"## What did I learn in school this year?\n",
|
||
"\n",
|
||
"[Our ideal about knowledge on computational science](http://hplgit.github.io/edu/py_vs_m/computing_competence.html)\n",
|
||
"\n",
|
||
"\n",
|
||
"Does that match the experiences you have made this semester?\n",
|
||
"<!-- dom:FIGURE: [figures/exam2.jpg, width=500 frac=0.7] -->\n",
|
||
"<!-- begin figure -->\n",
|
||
"\n",
|
||
"<p></p>\n",
|
||
"<img src=\"figures/exam2.jpg\" width=500>\n",
|
||
"\n",
|
||
"<!-- end figure -->\n",
|
||
"\n",
|
||
"\n",
|
||
"## Topics we have covered this year\n",
|
||
"\n",
|
||
"The course has two central parts\n",
|
||
"\n",
|
||
"1. Statistical analysis and optimization of data\n",
|
||
"\n",
|
||
"2. Machine learning\n",
|
||
"\n",
|
||
"## Statistical analysis and optimization of data\n",
|
||
"\n",
|
||
"The following topics will be covered\n",
|
||
"1. Basic concepts, expectation values, variance, covariance, correlation functions and errors;\n",
|
||
"\n",
|
||
"2. Simpler models, binomial distribution, the Poisson distribution, simple and multivariate normal distributions;\n",
|
||
"\n",
|
||
"3. Central elements of Bayesian statistics and modeling;\n",
|
||
"\n",
|
||
"4. Central elements from linear algebra\n",
|
||
"\n",
|
||
"5. Gradient methods for data optimization\n",
|
||
"\n",
|
||
"6. Monte Carlo methods, Markov chains, Metropolis-Hastings algorithm;\n",
|
||
"\n",
|
||
"7. Estimation of errors using cross-validation, blocking, bootstrapping and jackknife methods;\n",
|
||
"\n",
|
||
"8. Practical optimization using Singular-value decomposition and least squares for parameterizing data.\n",
|
||
"\n",
|
||
"9. Principal Component Analysis.\n",
|
||
"\n",
|
||
"## Machine learning\n",
|
||
"\n",
|
||
"The following topics will be covered\n",
|
||
"1. Linear methods for regression and classification;\n",
|
||
"\n",
|
||
"2. Boltzmann machines;\n",
|
||
"\n",
|
||
"3. Neural networks;\n",
|
||
"\n",
|
||
"4. Decisions trees and nearest neighbor algorithms\n",
|
||
"\n",
|
||
"5. Support vector machines\n",
|
||
"\n",
|
||
"## Learning outcomes and overarching aims of this course\n",
|
||
"\n",
|
||
"The course introduces a variety of central algorithms and methods\n",
|
||
"essential for studies of data analysis and machine learning. The\n",
|
||
"course is project based and through the various projects, normally\n",
|
||
"three, you will be exposed to fundamental research problems\n",
|
||
"in these fields, with the aim to reproduce state of the art scientific\n",
|
||
"results. The students will learn to develop and structure large codes\n",
|
||
"for studying these systems, get acquainted with computing facilities\n",
|
||
"and learn to handle large scientific projects. A good scientific and\n",
|
||
"ethical conduct is emphasized throughout the course. \n",
|
||
"\n",
|
||
"* Understand linear methods for regression and classification;\n",
|
||
"\n",
|
||
"* Learn about neural network;\n",
|
||
"\n",
|
||
"* Learn about basic data analysis, Bayesian statistics, Monte Carlo methods, data optimization and machine learning;\n",
|
||
"\n",
|
||
"* Be capable of extending the acquired knowledge to other systems and cases;\n",
|
||
"\n",
|
||
"* Have an understanding of central algorithms used in data analysis and machine learning;\n",
|
||
"\n",
|
||
"* Gain knowledge of central aspects of Monte Carlo methods, Markov chains, Metropolis and Gibbs samplers and their possible applications;\n",
|
||
"\n",
|
||
"* Work on numerical projects to illustrate the theory. The projects play a central role and students are expected to know modern programming languages like Python or C++.\n",
|
||
"\n",
|
||
"## Other courses on Data science and Machine Learning at UiO\n",
|
||
"\n",
|
||
"The link here <https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/> gives an excellent overview of courses on Machine learning at UiO.\n",
|
||
"\n",
|
||
"1. [STK2100 Machine learning and statistical methods for prediction and classification](http://www.uio.no/studier/emner/matnat/math/STK2100/index-eng.html). \n",
|
||
"\n",
|
||
"2. [IN3050 Introduction to Artificial Intelligence and Machine Learning](https://www.uio.no/studier/emner/matnat/ifi/IN3050/index-eng.html). Introductory course in machine learning and AI with an algorithmic approach. \n",
|
||
"\n",
|
||
"3. [STK-INF3000/4000 Selected Topics in Data Science](http://www.uio.no/studier/emner/matnat/math/STK-INF3000/index-eng.html). The course provides insight into selected contemporary relevant topics within Data Science. \n",
|
||
"\n",
|
||
"4. [IN4080 Natural Language Processing](https://www.uio.no/studier/emner/matnat/ifi/IN4080/index.html). Probabilistic and machine learning techniques applied to natural language processing. \n",
|
||
"\n",
|
||
"5. [STK-IN4300 – Statistical learning methods in Data Science](https://www.uio.no/studier/emner/matnat/math/STK-IN4300/index-eng.html). An advanced introduction to statistical and machine learning. For students with a good mathematics and statistics background.\n",
|
||
"\n",
|
||
"6. [INF4490 Biologically Inspired Computing](http://www.uio.no/studier/emner/matnat/ifi/INF4490/). An introduction to self-adapting methods also called artificial intelligence or machine learning. \n",
|
||
"\n",
|
||
"7. [IN-STK5000 Adaptive Methods for Data-Based Decision Making](https://www.uio.no/studier/emner/matnat/ifi/IN-STK5000/index-eng.html). Methods for adaptive collection and processing of data based on machine learning techniques. \n",
|
||
"\n",
|
||
"8. [IN5400/INF5860 – Machine Learning for Image Analysis](https://www.uio.no/studier/emner/matnat/ifi/IN5400/). An introduction to deep learning with particular emphasis on applications within Image analysis, but useful for other application areas too.\n",
|
||
"\n",
|
||
"9. [TEK5040 – Dyp læring for autonome systemer](https://www.uio.no/studier/emner/matnat/its/TEK5040/). 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.\n",
|
||
"\n",
|
||
"## Additional courses of interest\n",
|
||
"\n",
|
||
"1. [STK4051 Computational Statistics](https://www.uio.no/studier/emner/matnat/math/STK4051/index-eng.html)\n",
|
||
"\n",
|
||
"2. [STK4021 Applied Bayesian Analysis and Numerical Methods](https://www.uio.no/studier/emner/matnat/math/STK4021/index-eng.html)\n",
|
||
"\n",
|
||
"## Best wishes to you all and thanks so much for your heroic efforts this semester\n",
|
||
"\n",
|
||
"<!-- dom:FIGURE: [figures/Nebbdyr2.png, width=500 frac=0.6] -->\n",
|
||
"<!-- begin figure -->\n",
|
||
"\n",
|
||
"<p></p>\n",
|
||
"<img src=\"figures/Nebbdyr2.png\" width=500>\n",
|
||
"\n",
|
||
"<!-- end figure -->"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 2
|
||
}
|