Week 9 - Mar 21
Monday 3/1
MIT 6S191 Deep Generative Modeling (lecture 4) - vaes and gans.
MIT 6S191 De-biasing Facial Recognition Systems (lab 2): CNN, VAE, DB-VAE
Tuesday 3/2
College de France Approximations non linéaires et réseaux de neurones (lecture 4)
RL Course by David Silver lecture 1 - intro (22’/88’)
Future of Manufacturing@MIT - interesting landscape about Manufacturing and AI
Wednesday 3/3
Interpretable Machine Learning by Christoph Molnar. LIME reading to understand context of local surrogate models. SHAP chapter using Janus data.
Deep Reinforcement Learning by Thomas Simonini (Chapter 3 v1) on DQN with temporal limitation using LSTM, and experience replay. (replay buffer)
Thursday 3/4
Interpretable Machine Learning by Christoph Molnar. PDP chapter using Janus data.
Friday 3/5
RL - Sutton book (p220-223) - full vs sample backups, trajectory sampling, heuristic search
RL - Sutton book (p223+) - start of Approximate Solution Methods, why to use NN.
Week 10 - Mar 21
Monday 3/8
MIT 6S191 Deep Reinforcement Learning. Q-learning vs Policy Gradient.
Tuesday 3/9
College de France Ondelettes et échantillonnage (lecture 5)
RL Course by David Silver Introduction to Reinforcement Learning (lecture 1)
Installation of clustergit to detect local (=uncommited or unpushed) changes in repo
Wednesday 3/10
Deep Reinforcement Learning by Thomas Simonini (Chapter 4 v1) on four strategies to improve DQN (fixed Q-targets, double DQN, dueling DQN (DDQN), Prioritized Experience Replay (PER))
t-SNE using Janus data.
RL Course by David Silver Markov Decision Processes (lecture 2)
Friday 3/12
RL - Sutton book (p287-352) - Applications and case studies, end of the book
RL Course by David Silver Planning by Dynamic Programming (lecture 3)
Week 11 - Mar 21
Monday 3/15
MIT 6S191 Limitations and New Frontiers.
MIT 6S191 Pixels-to-Control Learning (lab 3): Cartpole and Pong
RL Course by David Silver Model-Free Prediction (lecture 4)
Tuesday 3/16
College de France Multi-résolutions (lecture 6)
Wednesday 3/17
Deep Reinforcement Learning by Thomas Simonini (Chapter 5 v1) - Policy Gradient
Thursday 3/18
Deep Reinforcement Learning by Thomas Simonini (Chapter 5 v1) - Policy Gradient notebooks
RL Course by David Silver Model-Free Control (lecture 5)
Friday 3/19
Deep Reinforcement Learning by Thomas Simonini (Chapter 6 v1) - Advantage Actor Critic (A2C) and Asynchronous Advantage Actor Critic (A3C)
College de France - l’apprentissage profond par Yann Lecunn 2016 - Pourquoi l’apprentissage profond ?
Week 12 - Mar 21
Monday 3/22
MIT 6S191 Evidential Deep Learning and Uncertainty (lecture 7).
Deep Reinforcement Learning by Thomas Simonini (v1 Part 5) - Advantage Actor Critic (A2C) - implementation and video
Tuesday 3/23
College de France Bases orthonormales d’ondelettes (lecture 7)
Wednesday 3/24
Deep Reinforcement Learning by Thomas Simonini (Chapter 7 v1) - Proximal Policy Optimization PPO
Stable baselines 3 - init and 1st tutorial
Thursday 3/25
setup headless raspberry pi to bridge wifi (tethering from phone) to ethernet (to wifi-router)
Stable baselines 3 - finalize init and go through documentation
Create a patch for a github project (by forking and pulling request)
Friday 3/26
Stable baselines 3 - Documentation > Examples
Rename my branches named master to main
Week 13 - Mar 21
Monday 3/29
MIT 6S191 Bias and Fairness (lecture 8).
Wednesday 3/31
College de France Parcimonie et compression d’images (lecture 8)