See also:

  • Optimization

ml for proofs bibliography


  • Speech recognition
  • Image recognition
  • branch prediction
  • phase transition recognition
  • Dimensionality reduction?
  • recommender systems
  • Fluid sim accelerate
  • system identification
  • learning program invariants



augmentation - sometimes you can apply trasnformations to the data in ways. For example rotating images if you want the answer to not depend on direction. Or adding noise if you want it to ignore noise. Warping.

In some problems it’s nice that you can cripple your data and train it to undo the crippling

  • colorizing images
  • interpolating frames
  • super resolution

Good Practice? What’s the right word here

Test and training sets cross validation data cleaning meta parameter tuning don’t set auxiliary goals.

Reading training curves debugging?


\[\hat{y}_i = f(x_i; \theta)\]

Classification discrete output

Regression - continous output

one hot encoding

Linear in WHAT?

Nearest neighbor



decision trees


PCA k-means clustering hierarchical clustering

dimsenionlaity reduction?


t-sne umap

Learning Theory

VC dimension


bias variance

Bayesian Shit

pyro pymc3 stan particle filters? probalistic programming


diffrax JAX powered differential equations.

Deep Learning



lstm gru blowup problem / vanishing gradient


Attention Capsule transformers

batch normalization

dropout grokking? overfit and keep going. Sometimes it gets better later. Bizarre. Don’t count on this. autoencoders GANs

graph neural networks?

transfer learning


Adversarial examples

Famous Models

word2vec node2vec


copilot gpt-3

wavenet DALL-E

alexnet vgg alphafold alpha zero / go BERT

Backprop Technqiues

adam sgd with momentum?


Tensorflow Pytorch JAX



  • Deep Learning Book

Reinforcement Learning

imitation learning reinforce helmut with 3 cameras.

Markov Decision Process Partially observed mdp (POMDP)

Temporal difference Reward function Value function Q function policy function Learn Dynamics - system identification

Q-learning sarsa policy gradient Actor critic

Monte-carlo search


Neural Network Verification with Proof Production


openai spinning up Sutton and Barto

Machine intellgience course

Old Reinforcement learning

I watched David Silver’s lectures on Reinforcement Learning.

Pretty interesting stuff.

We had already tried naive reinforcement learning for tic tac toe. We made a random player, and watched whether it won or lost. Then we’d pick only moves that ultimately won and tried to train a neural network to map board state to winning move. In hindsight, this was kind of a ghetto monte carlo policy learning. It worked kind of.

Big takeaways from the lectures:

Value functions and Q functions are things you may want to consider. They tell you the value of your current state. You may want to move to states of high value.

Very evocative of iterative methods for solving matrix equations. So if you’re looking for inspiration, look there. If you had the transition probabilities, it is a linear model for the probabilities.

There are table based methods for

There is also a layer of function approximation you can stack on there.

I think you could implement temporal difference learning using common libraries using $latex r_t + \gamma \max_a Q(S,a,\theta)$ as the truth value, and then update the truth values occasionally.


Reinforcement learning is when you get a rating of a move instead of the right answer. For example a supervised learning task would be to tell whether a picture is of a cat or not.

Also there is a stronger element of time occurring. Reinforcement learning often is sequential in nature. And the rewards may come later down the line rather than immediately

Explore exploit trade off. Exploration allows you to find new things. The new inns are only occasionally better than the stuff you already know about.

The many armed bandit is an example. You have many slot machines to choose from and 100 quarters. Should you try all the slot machines or

greedy method chooses curren best slot

epsilon greedy chooses best while occasionally choosing a random other

Policy is what actions you make given the current state. State is the encapsulation of the important information you’ve received from all previous measurements. Policy can be deterministic, a function from state to action, or probabilistic, the probability of an action given a state.

Value is the expected reward given a particular policy given your current state.

observations, actions, and rewards.

Three kinds of RL. Policy, Value, and Model based.

There are the questions of policy evaluation and policy optimization. They are related.

Given either a deterministic policy or probabilistic policy, you could hypothetically write down the exact probabilistic step from one time step to another.

There is a connection between Monte Carlo methods I’m more familiar with and the solution methods.

Monte Carlo methods replace expectations with samples. When used numerically, they use expectations as a stand in for a tough summation or integration.

One place where summatiuon occurs is in matrix multiplication. Row times column and then add them all up. Replace this addition with a sampling process.

The evolution of the probability distribution in a markov process can be written as a finite difference equation with a matrix full of the condition steps $latex P(x_{t+1} x_t)$

The matrix multiplication rule then is one that takes the prior distribution marginalizes it out to give the distribution in the next time step.

The optimization step is also somewhat . It is an interesting analogy that you can produce matrix like systems using (max,+) in the place of the usual (+,*). If you use softmax(a,b) = $latex \ln (e^a + e^b)$ it might even be somewhat invertible (although I have strong suspicions it might easily become numerically unstable).

This is for example used as a way of discussing shortest path problems using edge weight matrices. Mixing between the two is curious.

The world is an oracle that can give us samples for us.

Q(s,a) is a very clever, non obvious function.

To perform a step of the bellman equation without the model of the system, you need a function like that.

Q learning vs SARSA

Q learning is off policy. It pretends you’re using a greedy policy

SARSA is on policy

Deep RL course

Imitaiton learning. Learn human expert.

Can often lead to trajectory drifting off course. Clever way of fixing this includes building in some stability to the thing with three headed camera.

Dagger. Make a loop of using human expert. then let thing thing run, then give human the newly acquired data and have him label it. And so on.

Necessary because the poorly imitating robot will end up in states human expert would never see. Distributional mismatch

Model based. If you have am model you can use it. Optimal Control

shooting method, optimize over actions only. Plug in dynamics into cost function

collocation method, optimize over both path and controls with constraints

LQR  - dynamics linear, cost function quadratic. Can be back solved if initial condition problem.

Q_t matrix gives future cost assuming optimal policy in space of u and x

V_t matrix only in space of x

K matrix connects x to u. The feedback matrix. Can be built from blocks of Q.

iterative LQR / Differential dynamic programming (DDP uses second order expansion of dynamics)

go backwards to find the right K. Using it forwards to compute correct x and u. Iterate until convergence

Model Predictive Control. Do it all like you were playing chess. After each actual time step re-run the iterative LQR


learning the model.

So for example i don’t know the mass or inertia parameters (or timestep) of the cartpole in gym. I could build the general model though and then fit pairs of (x, x’, u)  to it to determine those. Under random policy.

Usually need to recurse on this step (use current best policy to get more data) like in dagger because random policy discovering dynamics won’t go to the same place that better policy does.

Better yet is to use mpc to replan at every step.

Can also directly train a parametrized policy function as part of the loop rather than using the more algorithmic iLQR

Neural network based model of the dynamics might be fine especially since you can backpropagate which is nice for the iterative LQR step.

in global models the planning stage will tend to try to exploit regions that are crappily modelled.

maximum entropy. May want to have the most random solution that doesn’t hurt cost?

Local models try to avoid this by just modelling gradients? And simply. But use a contrained optimization problem to make sure the robot stays in a region where the local estimates still apply. Trust regions. Defined using how unlikely the current trajectory is