#ml

Reinforcement Learning(RL) trains an agent to take actions in an environment, the agent does not get told what to action correct, it basically gets a reward signal instead.

Principal component analysis(PCA) allows us to take data with a lot of features say hundreds or thousands of features and reduce it to very small number of features say two or five features.

Recommender system predicts how a user would rank or respond to items they haven't seen yet, then surface with the highest predicted score

Anomaly detection looks at unlabeled dataset of normal examples, learns what normal looks like, then flags new examples that looks different

Clustering looks at a dataset and tries to find points that are similar to each other and group them together

Learned about the Gini index as a measure of impurity for decision trees

Learned a whole bunch about decision trees.

Learned about model diagnosis, when a model performs poorly, we can do the following: - Collect more training data - Try a smaller set of features - Add more features - Add polynomial features - Decrease the regularization parameter $\lambda$ - Increase $\lambda$ - Make the model bigger - Train longer

Learned about the steps involved in training a neural network which are: specify the model, specify the loss and cost function and then train the model on the training dataset to minimize the cost function.

Neuron is a computational unit which takes a vector of input, computes a weighted sum plus a bias, then apply a non-linear activation function to produce a single output.

Learned classification with logistic regression

Learned about regression with multiple input variables

No ML today

Not much

Explored intro to machine learning and types of machine learning models