[Source: rdn-consulting]
Getting started
- Why PhD at all? Before considering PhD | A CS PhD? PhD as pushing knowledge boundary
- PhD Comics
- Scholarhips at A2I2.
- A Survival Guide to a PhD by Andrej Karpathy
- Tips for PhD collected by Jia-Bin Huang
- 10 Tips for
Research and a PhD by Sebastian Ruder
- An
Opinionated Guide to ML Research by John Schulman
- So
you want to be a research scientist by Vincent Vanhoucke, aka, how to do modern ML/AI research
- Advice for early-stage Ph.D. students by Philip Guo
- How to have a good
CS career by Stefan Savage
- How to do Research At the MIT AI Lab
- How to do good (data mining) research, get it published by Eamonn Keogh
- How to Be a Good Graduate Student by Marie desJardins
- Advice for researchers and students, compiled by Michael Ernst
- Gentle introduction:
- A brief introduction ot machine learning by Suresh Venkatasubramanian
- Formal undegrad curicula: CMU | Edinburgh
- Maths books
- Linear algebra and its applications by Gilbert Strang.
- All of statistics by Larry Wasserman, 2010.
- Convex optimization by Stephen
Boyd and Lieven Vandenberghe.
- AI books
- AI: A modern approach by S.
Russell and P. Norvig, 4rd ed 2020.
- ML books
- The elements of statistical learning by Trevor Hastie, Robert Tibshirani, Jerome Friedman, 2nd ed, 2009.
- Machine Learning: a Probabilistic Perspective by Kevin Murphy, 2021-2022.
- Similar book: Bayesian
Reasoning and Machine Learning by David Barber, 2017.
- Reinfocement learning: An introduction
by Sutton and Barto, 2018
- Deep learning books
- Deep Learning by Ian
Goodfellow and Yoshua Bengio and Aaron Courville 2016.
- Dive into Deep by Zhang, A., Lipton, Z. C., Li, M., & Smola, A. J., 2019.
- CV books
- Computer Vision: Models, Learning,
and Inference by Simon J.D. Prince, 2012.
- Computer Vision: Algorithms and Applications by Richard Szeliski, 2020.
- NLP books
- Foundations of Statistical Natural Language Processing by Chris Manning and Hinrich Schütze, 1999.
- Introduction to natural language processing by Jacob Eisenstein, 2019.
- Courses:
- ML course by Andrew Ng
- Probabilistic graphical models by Daphne Koller
- AI impact in the world
- Prediction machines by Ajay Agrawal, Avi Goldfarb, and Joshua Gans, 2018.
- Superintelligence: Paths, Dangers, Strategies by Nick Bostrom, 2014.
- Deep medicine by Eric Topol, 2019.
- Cool videos
- Cool blogs
- Who is who and what they say:
- Yann LeCun, Geoff Hinton, Michael Jordan, Jürgen Schmidhuber, Yoshua Bengio and Andew Ng.
- Architects of intelligence by
Martin Ford 2018.
- AI/ML research:
- Modern AI/ML research
- Keeping yourself informed by following people on Twitter
- AI/ML topics
- Reinforcement learning.
- NLP
- Computer vision
- Machine reasoning
- Safety AI and value alignment
- Consciousness
- Quantum ML
- Cognitive architecture
- You and your research
by Richard Hamming, aka, how to win
Nobel Prize.
- Letters to a young scientist by
Edward O. Wilson, aka, you don't
need to be very smart.
- Advice for a young investigator
by Santiago Ramón y Cajal, aka, how
to do top-notch research when you're poor.
- How to pick right thesis topics:
- How to tame your PhD by Dr Inger Mewburn, also author of Thesis Whisperer blog.
- How to Build an Economic Model in Your Spare Time
- The Young Economist's Guide to Professional Etiquette
Innovation, creativity and futurists
- de Bono’s six thinking hats & lateral thinking.
- TRIZ - Theory of Inventive Problem Solving: 5 levels of inventiveness | 40 inventive principles
- Top 10 breakthroughs by MIT Tech Review
- Futurists
- The singularity is near by Ray Kurzweil, 2005
- Philosophy of sciences: Popper's falsifiability | Kuhn's paradigm
- Role model.
- Publishing papers
- CS conference ranking
- Australian CORE2014 conference ranking
- Google Scholar metric
- A fresh perspective: Writing medical journal papers, also here.
- The reviewing process.
- Writing
- Great advices from top people in ML.
- Writing for machine learning
- LaTex and LyX
- Elements of Style by William Strunk
- On writing by Stephen King.
- Coursera course: Writing in the Sciences by Dr. Kristin Sainani of Stanford
- Stages of writing a paper.
- NIPS 2013 author guide.
- Progress report.
- Technical paper.
- Kaggle.com -- the competition platform.
- A generic framework.
- Road map to data scientist
- Book: Mining of massive datasets by Jure Leskovec, Anand Rajaraman, Jeff Ullman
- YouTube: unreasonable effectiveness of data by Peter Norvig, Google Director of Research.
- Skill set for data analytics.
- Skill set for even more serious data research.
- Coursera's Data science stream by Johns Hopkins
- Network analysis
- A nice intro to GAM
- Healthcare analytics
- Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating by Ewout W. Steyerberg, 2009.
- Funding for data-driven discovery.
- Paul Graham
- Y Combinator
- Hi: Harvard innovation lab
- TechCrunch news
- Founders at work by Jessica Livingston
- Zero to One by Peter Thiel
- Steve Jobs by Walter Issacson
- How Google Works by Eric Schmidt & Jonathan Rosenberg
- Terrence Tao's career advice (maths focused)
- Academic job requirements.
- Getting Google's jobs, also the nature of jobs.
- Choosing the destination: Startup, Big Co, or Academia.
- Career paths
- How to Rise to the Top...and Stay There! by Alexander R. Margulis
- Postdocs
- Career questionnaire by Ben Langmead