added labs and demos

Author: pliugithub

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+[Online class](../../online_class.md) 
+
+# Unit 1.  Introduction
+
+Slides:  [[pdf]](../../lectures/Lect01_IntroML.pdf) [[Powerpoint]](../../lectures/Lect01_IntroML.pptx) 
+
+* What is Machine Learning?  [[YouTube]](https://youtu.be/WaVsEXB-SsI) [[Dropbox Video]](https://www.dropbox.com/s/ztq9s67d7kmdl0q/WhatIsML.mp4) (11 minutes)
+    * Exercise 1:  [[Question]](./Ex1_MLDefn.pdf)  [[Soln]](./Ex1_MLDefn_Soln.pdf)  
+* Types of Machine Learning Algorithms   [[YouTube]](https://youtu.be/4xEmC6kMtUk) [[Video]](https://www.dropbox.com/s/5pf57t43oav2os7/Types.mp4) (20 minutes)
+    * Exercise 2:  [[Question]](./Ex2_MLTypes.pdf)  [[Soln]](./Ex2_MLTypes_Soln.pdf)  
+* End of unit in-class exercise:  [[Question]](./Ex3_Problem.pdf).  Solution discussed in class
+
+

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+[Home](../sequence.md) - Introduction
+
+# Introduction 
+
+**Machine learning** refers to information processing algorithms that are designed by *learning from data*, not by physical models. 
+Examples of *supervised* machine learning tasks include *classification*, *regression*, and examples of *unsupervised* tasks include *clustering* and *principle components analysis* (PCA).
+Since this course will be taught in **Python**, we present a *Jupyter Notebook* demonstration of vector processing using the `numpy` library.