Here we list some events, podcasts, blogs, etc that might be of interest.
- TalkRL: The Reinforcement Learning Podcast. Very nice interviews with experts and practitioners of reinforcement learning.
- Talking Machines: Human Conversations about Machine Learning. Lead by experts in the field, very interesting discussions on the ever changing world of ML.
- Data Skeptic: Weekly interviews with leading researchers and practitioners on data science, machine learning, and artificial intelligence.
- A post that describes 10 top podcasts on AI, Data Science and Machine Learning can be found here.
Virtual meetings and seminars:
- RL Theory Virtual Seminars: Online seminars with the latest advances in reinforcement learning theory.
- Discrete Optimization Talks (DOTs): Two weekly 30-minute talks which include theoretical, computational, and applied aspects of integer and combinatorial optimization.
- Online Seminar on Mathematical Foundations of Data Science: A weekly online seminar on random topics on mathematical foundations of machine learning, statistics and optimization.
- SPS Virtual Seminar Series: Virtual Seminar series organized by the Stochastic Programming Society
- MTL MLOpt: meetings and talks by members of the Montréal Machine Learning and Optimization (MTL MLOpt)
- One World Optimization Seminar: Very nice talks by leading figures in theoretical optimization.
Other online resources:
- stateoftheart.ai: Newest developments in the fields of AI and ML in an easily consultable repository for state of the art, quantifiable results across tasks on AI, ML and RL.
- Spinning Up in Deep RL: Educational resource produced by OpenAI that makes it easier to learn about deep reinforcement learning (deep RL).
- Vectorization in Python: a very nice resource for scientific computing using Numpy in Python.
- Depth First Learning: Compendium of curricula to help understand Machine Learning.
This is of course a very narrow list of all available options on the web.