Artificial Intelligence

Machine Learning

Machine learning (ML) includes methods that leverage data to improve performance on some set of tasks.

Resources

• Rice University lectures
• ML interviewsGitHub Repo
• Articles
  • https://homes.cs.washington.edu/~pedrod/papers/cacm12.pdf

The 3 Categories of ML

1. Supervised Learning
2. Unsupervised Learning
3. Reinforcement Learning

General ML Concepts

• Cross Validation
• Active Learning

ML Algorithms

Regression

• Regression
• Polynomial Regression
• Decision Tree Regression
• Random Forest Regression

Classification

• Logistic Regression
• K-Nearest Neighbors
• Support Vector Machine
• Decision Tree Classification

Clustering Algorithms (good article here: https://web.stanford.edu/class/cs345a/slides/12-clustering.pdf)

• K-Means Clustering
• Hierarchical Clustering

Two methods of clustering:

• Hierarchical clustering
• Point assignment

Association Rule Learning

• Apriori
• Eclat

ML Models (as outlined by Wikipedia)

• Neural Network
• Decision Tree
• Support Vector Machine
• Regression Analysis
• Bayesian Network
• Genetic Algorithm

Frameworks

• Dask
• Tensorflow
• Pandas
• NumPy
• scikit-learn
• Dask
• Xarray
• Amazon Web Services (AWS)

Limitations

• Bias
• Variance

Misc. Topics

• Conway’s Game of Life
• Autonomous Vehicles

Reflection

I asked this guy in Yash’s group, “what kind of machine learning do you do”? He scoffed at me, because I told him I was undergrad, and he asked me what kind of machine learning do I know. That kind of pissed off my Ego, but in coming up with an answer, it made me realize that there is still so much I don’t know much about machine learning.

In the future, add JAX as a language since you want to go into ML.

Interview Prep