mhjensen
162 lines
8.6 KiB
Plaintext
162 lines
8.6 KiB
Plaintext
TITLE: Applied Data Analysis and Machine Learning: Introduction to the course, Logistics and Practicalities
|
|
AUTHOR: Morten Hjorth-Jensen {copyright, 1999-present|CC BY-NC} at Department of Physics, University of Oslo & Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University
|
|
DATE: today
|
|
|
|
|
|
|
|
!split
|
|
===== Overview of first week =====
|
|
|
|
!bblock
|
|
* Thursday: First lecture: Presentation of the course, aims and content
|
|
* Thursday: Second Lecture: Start with simple linear regression and repetition of linear algebra
|
|
* Friday: Linear regression
|
|
* Computer lab: Tuesday. First time: Tuesday August 27.
|
|
!eblock
|
|
|
|
|
|
!split
|
|
===== Lectures and ComputerLab =====
|
|
|
|
!bblock
|
|
* Lectures: Thursday (12.15pm-2pm) and Friday (12.15pm-2pm).
|
|
* Weekly reading assignments 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-6pm), VB IT-auditorium 3
|
|
* 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
|
|
|
|
!split
|
|
===== Course Format =====
|
|
|
|
!bblock
|
|
* Three compulsory projects. Electronic reports only using "devilry":"https://devilry.ifi.uio.no/" to hand in projects and "Git":"https://github.com/" for repository and all your material.
|
|
* Evaluation and grading: The three projects are graded and each counts 1/3 of the final mark. No final written or oral exam.
|
|
o For the last project Each group/participant submits a proposal or works with suggested (by us) proposals for the project.
|
|
o If possible, we would like to organize the last project as a workshop where each group makes a poster and presents this to all other participants of the course
|
|
o Poster session where all participants can study and discuss the other proposals.
|
|
o Based on feedback etc, each group finalizes the report and submits for grading.
|
|
* Python is the default programming language, but feel free to use C/C++ and/or Fortran or other programmin languages. All source codes discussed during the lectures can be found at the webpage and "github address":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs" of the course.
|
|
!eblock
|
|
|
|
!split
|
|
===== Teachers and ComputerLab =====
|
|
|
|
!bblock
|
|
|
|
_Teachers :_
|
|
|
|
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"
|
|
|
|
|
|
|
|
|------------------------------------------------------|
|
|
| day | Time |
|
|
|----------------------------------------------------|
|
|
| Group 1: Tuesday | 8am-10am |
|
|
| Group 2: Tuesday | 10am-12pm |
|
|
| Group 3: Tuesday | 12pm-2pm |
|
|
| Group 4: Tuesday | 2pm-4pm |
|
|
|-------------------------------------------------|
|
|
|
|
!eblock
|
|
|
|
!split
|
|
===== Deadlines for projects (end of day) =====
|
|
|
|
!bblock
|
|
|
|
o Project 1: September 30 (graded with feedback)
|
|
o Project 2: November 4 (graded with feedback)
|
|
o Project 3: December 2 (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.
|
|
|
|
!eblock
|
|
|
|
|
|
|
|
!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++
|
|
|
|
!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
|
|
!eblock
|
|
|
|
|
|
!split
|
|
===== Topics covered in this course: Machine Learning =====
|
|
|
|
!bblock
|
|
The following topics will be covered
|
|
* Linear Regression and Logistic Regression
|
|
* Neural networks and deep learning
|
|
* Decisions trees and nearest neighbor algorithms
|
|
* Support vector machines
|
|
* Bayesian Neural Networks
|
|
* Boltzmann Machines
|
|
* Dimensionality reduction, from PCA to cluster models
|
|
!eblock
|
|
|
|
!split
|
|
===== 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
|
|
!eblock
|
|
|
|
|
|
!split
|
|
===== Other courses on Data science and Machine Learning at UiO =====
|
|
|
|
The link here URL:"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.
|
|
|
|
o "STK2100 Machine learning and statistical methods for prediction and classification":"http://www.uio.no/studier/emner/matnat/math/STK2100/index-eng.html".
|
|
o "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.
|
|
o "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.
|
|
o "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.
|
|
o "STK-IN4300 œôòó 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.
|
|
o "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.
|
|
o "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.
|
|
o "IN5400/INF5860 œôòó 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.
|
|
o "TEK5040 œôòó Dyp l�æring 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.
|
|
o "STK4051 Computational Statistics":"https://www.uio.no/studier/emner/matnat/math/STK4051/index-eng.html"
|
|
o "STK4021 Applied Bayesian Analysis and Numerical Methods":"https://www.uio.no/studier/emner/matnat/math/STK4021/index-eng.html"
|
|
|
|
|
|
|
|
|