MResMLBDinPhysicalSciences

KO | EN

gitlite — search

#javascript #typescript #llm #golang #go #rust #macos #windows #awesome #kubernetes #awesome-list #vue #ios #open-source

MResMLBDinPhysicalSciences

(★ 16)

Machine Learning MRes Material

MResMLBDinPhysicalSciences Latest Version Download

Download Latest Version (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Machine Learning and Big Data in the Physical Sciences - Pre-course Material

This is the pre-course material for the Machine Learning and Big Data in the Physical Sciences MRes at Imperial College: https://www.imperial.ac.uk/physics/students/current-students/taught-postgraduates/machine-learning-big-data/

Python is used extensively for machine learning and analysis of large datasets both in scientific research and industry! Whether you're familiar with python or not, we **strongly recommend** that you review the following Python tutorials so that you're comfortable with
progamming in python before you start the course.

Doing so will help you prepare for starting the course in October and to acheive more during your time at Imperial.

## Jupyter notebooks

All of the exercises in the pre-course materials are written in jupyter notebooks. These allow you to try out python for yourself, without needing to download anything to your laptop. You can try out any of the notebooks by clicking this button -> [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/ImperialCollegeLondon/MResMLBDinPhysicalSciences/main?filepath=PreCourse)

Clicking on that button will open a web browser, in which you can run the jupyter notebooks (the files ending in `.ipynb`). Note that it might take a few minutes to setup the environment for you. You might need to try it more than once if you get an error.

You can alternatively open the notebooks with [Google Colab](https://colab.research.google.com/) if you have a Google account, by clicking  [this link](https://colab.research.google.com/) and searching for `ImperialCollegeLondon/MResMLBDinPhysicalSciences` in the GitHub tab.

If you prefer to download the notebooks to your laptop instead of using Binder (for example to save your work). You can do so by using Git

```
git clone git@github.com:ImperialCollegeLondon/MResMLBDinPhysicalSciences.git
```

## Introductory Python

First, take a look at the [PreCourse/Python/Basic_Python](https://github.com/ImperialCollegeLondon/MResMLBDinPhysicalSciences/blob/main/PreCourse/Python/Basic_Python.pdf) slides to learn about basic programming in python. There are four jupyter notebooks to work through;
  * [`basic_python.ipynb`](https://github.com/ImperialCollegeLondon/MResMLBDinPhysicalSciences/blob/main/PreCourse/Python/basic_python.ipynb)
  * [`classes.ipynb`](https://github.com/ImperialCollegeLondon/MResMLBDinPhysicalSciences/blob/main/PreCourse/Python/classes.ipynb)
  * [`exceptions.ipynb`](https://github.com/ImperialCollegeLondon/MResMLBDinPhysicalSciences/blob/main/PreCourse/Python/exceptions.ipynb)
  * [`lambdas.ipynb`](https://github.com/ImperialCollegeLondon/MResMLBDinPhysicalSciences/blob/main/PreCourse/Python/lambdas.ipynb)

that will teach you about some of basic features of python that you will need during the MRes course.

## Scientific Python

While python is a simple and easy to learn programming language, the many scientific libraries available for python make it extremely powerful for machine learning and data science. Take a look through the [PreCourse/Python/Scientific_Python](https://github.com/ImperialCollegeLondon/MResMLBDinPhysicalSciences/blob/main/PreCourse/Python/Scientific_Python.pdf) slides to learn about  `numpy` and `scipy`.

You can also attempt the exercises found in the [`Worksheet.ipynb`](https://github.com/ImperialCollegeLondon/MResMLBDinPhysicalSciences/blob/main/PreCourse/Python/Worksheet.ipynb) jupyter notebook to get to grips with these packages. There may be some concepts you aren't familiar with that are required for these exercises so don't worry if you can't solve them just yet!
 
## Statistics in Python

We use python a lot in statistical analyses of data. There `scipy.stats` package is very useful for dealing with random variables and probability distributions. Have a look at the [PreCourse/Statistics/StatisticsInPython](https://github.com/ImperialCollegeLondon/MResMLBDinPhysicalSciences/blob/main/PreCourse/Statistics/StatisticsInPython.pdf) slides (and [`StatisticsInPython.ipynb`](https://github.com/ImperialCollegeLondon/MResMLBDinPhysicalSciences/blob/main/PreCourse/Statistics/StatisticsInPython.ipynb) jupyter notebook) to see some basic statistics with python.

# Other material for you during your MRes - After you arrive at Imperial

We **strongly advise** you to take a look at additional courses / material below to brush up (or learn) the skills you'll need for the MRes. Some of this will be covered again during your taught courses but having seen some of it in advance will definitely help! Some of this material will not be accessible to you until you have your Imperial College logon so don't worry if you can't do those already.

## Learning Python Courses

If you do not feel confident with your Python coding ability after going through the Pre-course material above, and you could not complete the exercises, you should register for the following courses;  
  * [Introduction to Python](https://www.imperial.ac.uk/students/academic-support/graduate-school/professional-development/masters-students/introduction-to-python-online-course/). This is an online course that can be taken at your own pace, which you can access as soon as you have an Imperial Logon. 
  * [Object-Oriented Python](https://www.imperial.ac.uk/students/academic-support/graduate-school/professional-development/doctoral-students/research-computing-data-science/courses/object-oriented-python/). You can sign up to this course once you arrive at Imperial by filling in the booking form (for staff, master's and undergraduate students) on the [main RCDS courses page](https://www.imperial.ac.uk/students/academic-support/graduate-school/professional-development/doctoral-students/research-computing-data-science/courses/). There course is run both in-person and online throughout the year. 
 
Both of these courses can be followed in a Self-study mode online, and the material can be viewed once you have an Imperial College Login.

## Other Imperial skills courses

Once you have fully enrolled at Imperial,  you will have access to many courses on Research practices, skills and scientific writing from the Graduate School. You will find out more about these courses at the start of the MRes programme, but you can already take a look at the material available if you want to dig a bit deeper into courses related to the MLBD MRes. You can find all of the courses on the [Early Career Research Institute]([https://www.imperial.ac.uk/early-career-researcher-institute/)) web pages.

### Preparing for research / thinking about a PhD
 There are several courses designed for students who are thinking about continuing in research after the MRes. The courses are listed on the page [here](https://www.imperial.ac.uk/early-career-researcher-institute/who-we-support/masters-students/)

### Computing and data science 
  There are a number of courses from the [RCDS](https://www.imperial.ac.uk/early-career-researcher-institute/learning-and-development/courses-by-programme/research-computing-and-data-science/) that have limited places for Master's students. These include courses on machine learning, data analysis, python and other programming languages. For students who want a bit of extra material beyond that taught during the MRes, these courses are a good way to engage in additional learning.

## Mathematics for Statistics and Machine Learning

While the MRes is focused on the practical application of machine learning and statistics for data analysis, we can't avoid doing some mathematics. Whether you don't have a very maths based background or you feel a bit rusty on your maths, you should  brush up in these areas before you start the course. Imperial College runs an online course on coursera which we highly recommend for you "Mathematics for Machine Learning Specialization": https://www.coursera.org/specializations/mathematics-machine-learning

Once you have your Imperial College Login, you will be able to access the course and we recommend you take Course 1 and Course 2 from Mathematics for Machine Learning Specialization.

The main mathematics areas to brush up on are those typically taught in undergraduate STEM courses; 
   * Linear algebra (matrix multiplication and eigenvalues/eigenvectors) 
   * Basic calculus (differentiation, chain rule, integration)
   * Ordinay differential equations

These subjects will be covered in the above coursera course but some revision in these over the summer will help.

MResMLBDinPhysicalSciences

MResMLBDinPhysicalSciences Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

MResMLBDinPhysicalSciences

MResMLBDinPhysicalSciences Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

Download README