[Source: rdn-consulting]

Home   

• 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

• 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.
• Pattern Recognition and Machine Learning
  by Christopher M. Bishop, 2006.
  
• Machine Learning: a Probabilistic Perspective by Kevin Murphy, 2022-2023.
• 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: Foundations and Concepts by C. Bishop & H. Bishop, 2023.
• Deep Learning with Python by Francois Chollet, 2021.
  
• Dive into Deep Learning by Zhang, A., Lipton, Z. C., Li, M., & Smola, A. J., 2021.
• CV books
• Computer Vision: Algorithms and Applications by Richard Szeliski, 2022.
• NLP books
• Introduction to natural language processing by Jacob Eisenstein, 2019.
• Speech and Language Processing by Dan Jurafsky and James H. Martin, 2023.
  
• Robotics books
• Robotics, Vision and Control by Peter Corke, 2017.
  
• Other foundations
• Information Theory, Inference, and Learning Algorithms by David J.C. MacKay.
• Bayesian Data Analysis by Andrew Gelman.
• Nonlinear Programming by Dimitri P. Bertsekas.
• Causality by Judea Pearl.
• AI impacts 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.
• Human Compatible: Artificial Intelligence and the Problem of Control, Stuart J. Russell, 2019.
• The Alignment Problem: Machine Learning and Human Values, Brian Christian, 2020.
• Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating by Ewout W. Steyerberg, 2009.
• Courses:
• ML course by Andrew Ng
• Probabilistic graphical models by Daphne Koller
• Stanford ML class
  
• Cool videos
• Video resources.
• Cool blogs
• Neil Lawrence's blog.
• Sebastian Ruder's blog.
• inFERENCe.
• 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 pick a biostatistics thesis topic
• Pick problem first.
• 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

• 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
• Funding for data-driven discovery.

• Paul Graham
• How to get startup ideas -- living in the future.
• 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