Applied Machine Learning

Week 1:

• Learn the fundamentals and basics of machine learning such as terms and types of machine learning. Most of this was a refresher and the core of the course would start the following week.

Week 2:

• Started talking about linear regression and the different forms it comes in

  • Discussed the Normal equation and computation complexity of performing it in different methods

  • Gradient Descent

    • Stochastic, Batch, and Mini-Batch

  • Polynomial Regression

Week 3:

• Continued on the topic of Linear Regression and its forms

  • Regularized models

    • Ridge Regression

    • Lasso Regression

    • Elastic Net

    • Early Stopping

  • Logistic Regression

    • Estimating Probabilities

    • Decision Boundaries

    • Softmax Regression

Week 2-3 Notes

Week 4:

• Started on the Topic of Decision Trees

  • Training and Visualizing

  • Predicting

  • Estimating Probabilities

  • CART Training algorithm

  • Computation complexity

  • Gini impurity with entropy alternative

  • Regularization

  • Regression

Week 4 Notes

Week 5:

• Started on the Topic of Ensemble Learning

  • Decision Function

  • Single Classifier

    • Bayes Classifier

    • K-Nearest Neighbors

    • Decision Tree

    • Neural Network

    • Support Vector Machine

  • Ensemble Leas to better results

    • Selection & Fusion

Week 7:

• Bayesian Learning

  • SVM & Finding hyperplanes

  • Optimazation for hyperplanes

  • Kernels

    • Linear Gaussian Exponential Polynomial Hybrid Kernel Sigmoidal

  • Naïve Bayes

Week 10:

• Artificial Neural Networks

  • Foundation

  • Perceptron

  • Training functions

  • Backpropagation

Week 11:

• Genetic Algorithms

  • Representation

  • Fitness

  • Reproduction

  • Convergence

Week 12:

• Deep Learning

  • Very data driven

  • Supervised, unsupervised, Reinforcement

  • Convolutional Neural Networks

  • Activation Functions

  • Recurrent Neural Networks

    • LSTM