NUS_EE4211_DataScienceForIoT_学习笔记

This blog will update continuously during the course period.

Homework 1

H1 includes two parts, a reading material and a simple linear regression problem.

Python Machine Learning book by Sebastian Raschka. (Ch1)

In Chapter 1, we will learn about the main concepts and three different types of machine learning. Together with a basic introduction to the relevant terminology, we will successfully use ML techniques for practical problem solving.

Concept

Machine learning evolved as a subfield of artificial intelligence in the second half of the twentieth century that involved the development of self-learning algorithms to gain knowledge from that data in order to make predictions.

3 types of ML

1. supervised
   Main goal: learn a model from labeled training data that allows us to make predictions about unseen or future data.

• Classification task : with discrete class labels, such as e-mail spam-filter.
  
• Regression analysis: outcome signal is a continuous value. Use the intercept and slope learned from the given predictor variable and response variable to predict the outcome variable of new data.
  

2. reinforcement
   Main goal: Develop a system (agent) that improves its performance based on interactions with the environment.
   The information about current state of environment typically also include a so-called reward signal. The feedback is not a correct ground truth label or value, but a measure of how well the action was measured by a reward function.
   
   Example: chess engine. The agent decides upon a series of moves depending on the state of the board (the environment), and the reward can be defined as win or lose at the end of the game
3. unsupervised
   Explo