Prashant Singh Rana, PhD
Assistant Professor,
Computer Science and Engineering Department,
Thapar Institute of Engineering and Technology University, Patiala, Punjab.
Computer Science and Engineering Department,
Thapar Institute of Engineering and Technology University, Patiala, Punjab.
- Click Here to join Rana Research Group for ML News, Jobs, Article, Internship, Code and many more
- Click Here to join Ri@Ti group (Research Initiative @ Thapar Institute) for internship and jobs positions.
- Click Here for Resources Leading India
UNIT IV: Important Resources for Machine Learning
UNIT V: Things to do for a Research Scholar
UNIT VI: How to Choose a Research Topic
UNIT VII: Gold Mine for Researcher
UNIT VIII: Research in Computational {Biology, Chemistry, MD, Modelling & Simulation, more}
UNIT I: Skill sets required for a computer science researcher
- Python, R, Matlab/Octave.
- Optimization Techniques: Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Differential Evaluation (DE), Ant Colony Optimization (ACO), Artificial Bee Colony Optimization (ABC), etc.
- Graph Plotting: Python (Matplotlib), R(ggplot), Matlab, Excel.
- Latex for Scientific Writing.
- Strong Technical Writing.
UNIT II: Learn Python
- Download & Install Python
Anaconda (Highly Recommended) → https://www.continuum.io/downloads
Default Editor → Spyder
To Start IDLE → Open “Anaconda Prompt” and write “idle” - Learn Basics of Python in 2 hr [Important]
It contains 16 basic programs in python that help to understand the syntax, loops, conditional checking, data structures, file handling in python.
http://bit.ly/Rana-Python - Byte of Python
Very good and easy tutorial to learn advances in python.
http://python.swaroopch.com/ - Scipy, NumPy, Matplotlib [Important]
Specialised libraries for python for various operations such as interpolation, optimization, linear algebra, signal processing, Fourier transformation, etc
http://www.scipy.org → Go to Documentation
http://www.numpy.org
http://matplotlib.org → for Plotting; Go to Gallery and ExamplesBasics Plotting using Python
http://www.ast.uct.ac.za/~sarblyth/pythonGuide/PythonPlottingBeginnersGuide.pdf
https://plot.ly/python/ - Scientific Programming, Analysis and Visualization with Python [Important]
Part I, Part II
[Book] Learning SciPy for Numerical and Scientific Computing Second Edition Click Here to download.
[Book] A primer on scientific programming with Python Click Here to download. - Machine Learning using Python [Important]
http://scikit-learn.org - Python Code for Optimization [Important]
Click Here to Download. - PCA and LDA using Python
Principal Component Analysis and Least Discernment Analysis using Python
http://www.analyticsvidhya.com/blog/2016/03/practical-guide-principal-component-analysis-python/ http://sebastianraschka.com/Articles/2014_pca_step_by_step.html
http://sebastianraschka.com/Articles/2014_python_lda.html - Graph Theory using Python
Algorithm and Problems
Graph Tools → http://graph-tool.skewed.de → Go to Documentation
iGraph → http://igraph.org/python/
NetworkX → https://networkx.github.io → Documentation + Examples + Tutorial - Python Packages for research [Most Important]
For research on Scientific/Engineering problems such as AI, Bioinformatics, Chemistry, Electronics Design, GIS, Human Machine Interface, Image Recognition, NPL, etc.
https://pypi.org
UNIT III: Learn R
- R and R-Studio
R → https://cran.rstudio.com/bin/windows/base/
RStudio → https://www.rstudio.com/products/rstudio/download/ - Books
Books on R, Python, Machine Leaning, Big Data Analytics
Go to → http://bit.ly/MachineLearningBooks- 01 – The Machine Learning – Starter Kit
- 02 – Data Mining with Rattle and R
- 03 – Elements of Statistical Learning- data mining, inference and prediction
- 04 – An Introduction to Statistical Learning with Applications in R
- 05 – Applied Predictive Modeling
- 07 – R for Everyone Advanced Analytics and Graphics
- 08 – Reproducible Research with R and RStudio
- Explore “Others Books on Machine Learning”
- Explore “Books-Maths-Linear Lagebra-Probalility”
- Explore “Books-Softcomputing”
- Sample Dataset
Data set for Machine Learning practical
http://bit.ly/SampleDataSet - Machine Learning Models Code in R
Machine Learning Models using R Coding + Hands on R programming.
http://bit.ly/MachineLearningCodeInR - Machine Learning Models in R [Important]
http://bit.ly/MachineLearningModelsInR - Rattle Videos
Videos on Rattle, R Studio, Create R Package
http://bit.ly/MachineLearningUsingRattle - R Code for Numerai
Click Here to download - R Packages for research [Most Important]
For research on Scientific/Engineering problems such as AI, Bioinformatics, Chemistry, Electronics Design, GIS, Human Machine Interface, Image Recognition, NPL, etc.
R Packages for Research
UNIT IV: Important Resources for Machine Learning
- Machine Learning MOOCs on Coursera.org [Most Important]
Recommeded courses from University of Washington.
Machine Learning @ Coursera - Videos on Big Data [Most Important]
Learn Big Data Analytics using Top YouTube Videos, TED Talks & other resources
http://bit.ly/BigDataAnalyticsVideos - The Talking Machines
Discussion on latest topics on Machine learning people from Academics / Industry.
www.thetalkingmachines.com - Kaggle [Important]
Machine Learning Competitions. Helpful in selecting research topics.
Join Mailing groups for recent article/ research on machine learning, R, and many more.
www.kaggle.com - R bloggers [Important]
Join Mailing groups for recent article/ research on machine learning, R, and many more.
www.r-bloggers.com - KDNuggests
Join Mailing groups for recent article/ research on machine learning, R, and many more.
www.kdnuggets.com - Analytics Vidhya
Join Mailing groups for recent article/ research on machine learning, R, and many more.
www.analyticsvidhya.com - Machine Learning Competitions (Crowd Analytics) [Most Important]
Helpful in selecting research topics. - Data Set for Machine Learning [Most Important]
http://bit.ly/DataForMachineLearning - Machine Learning Mastery
Join Mailing groups for recent article/ research on machine learning, R, and many more.
www.machinelearningmastery.com - Join Mailing Groups [very imp]
Helpful in selecting research topics, recent news and updates - Mathematics for Machine Learning [Most Important]
- Explore “Books-Maths-Linear Lagebra-Probalility”
- Self Evaluation in maths for machine learning.
http://bit.ly/MathsForMachineLearning1 - How to improve maths skills?
http://bit.ly/MathsForMachineLearning2 - Skills Required for Machine Learning jobs.
http://bit.ly/MathsForMachineLearning3 - Introduction to linear algebra with R
http://bit.ly/MathsForMachineLearning4 - Workshop on R
http://bit.ly/MachineLearningWorkshop1
UNIT V: Things to do for a Research Scholar
- Maintain a notebook for daily work plan.
- To improve Technical Writing: One Page Writing of Abstract + Conclusion
- Learning by doing.
- Learn Python/NumPy/Scipy/Matplotlib.
- Learn Rattle/R/Weka.
- Learn Matlab and Octave (Alternative to Matlab)
- Learn Latex.
- Bi-Weekly Presentation.
- Join mailing list e.g.:
Indeed.com, R-Bloggers.com, Kaggle.com, Bioclues.org, kdnuggets.com - Learn Optimization Techniques.
- Learn Softcomputing Techniques.
- Learn Genetic Algorithm
- Learn PSO, ABC, ACO, DE.
- Learn Multi-Objective Optimization (NSGA II)
- To learn FAST
- Learn from slides.
- Learn from Youtube / Videos.
- Learn code sharing (Githhub, Shiny, etc)
- Githhub: https://github.com
- Shiny: http://shiny.rstudio.com
UNIT VI: How to Choose a Research Topic
- Explore the Competitions and choose a topic
- For Optimization Problems
- Encyclopedia of Optimization
This is a very good book for optimization problems. Explore ‘Subject Index’ section is very useful.
- Encyclopedia of Optimization
- For Machine Learning Problems
- Machine Learning Salon
This is very good resource for machine learning. It contain information about research groups, blogs, article, people, problem domain and many more. - Data Tau
Great resource for machine learning in the form on Blogs & Forums. It may help to choose research topic.
- Machine Learning Salon
- For Computer Networks
Those who are interested in Computer Networks (Security, Modelling, Analysis, Simulation, and many more). Kindly explore the following books. - Explore the R Packages of your interest
For research on Scientific/Engineering problems such as AI, Bioinformatics, Chemistry, Electronics Design, GIS, Human Machine Interface, Image Recognition, NPL, etc.
R Packages for Research - Explore the Python Packages of your interest
For research on Scientific/Engineering problems such as AI, Bioinformatics, Chemistry, Electronics Design, GIS, Human Machine Interface, Image Recognition, NPL, etc.
https://pypi.python.org/pypi
UNIT VII: Gold Mine for Researcher
- For Research Papers
- Go to www.sci-hub.tw
Search using DOI.
Example (Search for): 10.1016/j.bbapap.2014.07.010 - Go to booksc.org
Search using title.
Example (Search for): Quality assessment of modeled protein structure
- Go to www.sci-hub.tw
- For Books
- For Thesis
UNIT VIII: Research in Computational {Biology, Chemistry, MD, Modelling & Simulation, more}
- Computational Chemistry Tools and Softwares
- Molecular Dynamics Softwares
Most Important
“If you give 100 hour per week, you can complete your PhD in three years” – Prof. Bhim Singh, IIT Delhi.
Finally
- “Mathematics is the queen of all the sciences” – Anonymous
- Whenever you have time, solve/explore maths problems, solve/explore graph problems, do maths using R/Python/Matlab/Octave, explore competitions, explore dataset.
- “Great minds discuss ideas, Average minds discuss events, Small minds discuss people”
- “Success is a journey…not a Destination!!!!”