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PDF Editor FAQ
What should I do if I only have 4 months to learn data science and machine learning?
This website contains links to the Full Textbook, Instructor Videos from the online course they taught at Stanford in 2015, & slide Decks for the “Introduction To Statistical Learning.[1]” This is the textbook mentioned earlier in a response. The authors post a PDF copy online for free, so don’t buy it off of Amazon unless you prefer a printed copy.I took a course at UT Austin that was based on this book, and it was simultaneously accessible and rigorous. One nice note is that its popularity has warranted many discussions of the text online for further reading. I also found it very useful to use R Markdown for each chapter as I went through the labs and problem sets.Footnotes[1] Introduction to Statistical Learning
Is the B. S. Grewal engineering maths book ok for brushing up on maths if I am going for a PhD in operations research?
Greetings,I have some experience in the field of operations research. I haven’t read B.S. Grewal myself but I did go through its table of contents. There is lot of surplus material there which wouldn’t be needed in your Ph.D. for operations research (at-least initially). I would recommend picking up any good operations research textbook and going through mathematical background section of the book.General Advice: It is my understanding that if you are yet to start your Ph.D. you will require a good background in linear algebra and statistics to really understand the essence of OR subjects. Additionally, some background in computer programming won’t hurt. You could start with Python and then branch out to others as per requirement.Following are the books I would recommend:Before starting your Ph.D.For Statistics:Probability and Statistics for Engineers and Scientists - Sheldon Ross (Chapters 1–4)Introduction to Probability Models - Sheldon Ross (Chapters 1–3)For optimisation:Introduction to optimisation - Chong and Zak (4th Edition - Part 1: Mathematical Review).You could also watch the videos on this link (NPTEL :: Computer Science and Engineering) - Mathematical Background section.After starting your Ph.D.Probability and Stochastic ModelsIntroduction to Probability Models - Sheldon Ross (Chapters 1–7)OptimisationLinear ProgrammingIntroduction to Linear Optimization - Bertsimas and Tsitsiklis (Chapters 1–5).Linear Programming - G. HadleyLinear Programming - V. ChvatalInteger ProgrammingInteger Programming - Conforti, Cornuejols, ZambelliInteger Programming - Laurence WolseyConvex Optimisation - Boyd (This one is a bit advance)
Is it worth it to own both Introduction to Statistical Learning and Elements of Statistical Learning?
As pdf of both books are available online ,free of cost by the authors ,so it will be better that you download both and just try to read 1-2 chapter of each and decide which is more comprehensible to you. If you just want to know the statistical techniques , how to use them and what are theirs pros and cons instead of going through rigorous mathematical calculation then I think Introduction to statistical learning will be a good choice for you. Introduction to statistical Learning also discusses statistical method explained in book using R so its kind of learning by doing experience for a reader. I prefer pdf file as I can read it wherever I want in preference to carrying a physical form .
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