This the deck shared during Lecture 1 (September 20) in the online class given by Christopher Himmel. Please go to my website DeeperSideofLearning.com to get more information.

1. Machine Learning:
Zero to Deep Dive
Lecture 1 - Introduction to Machine Learning
20 September 2018, 6:00pm Pacific

2. Lecture 1 Agenda
● About me
● Class Logistics
○ Student requirements
○ Schedule
○ Topics of Lectures
● History of AI
○ Artificial Intelligence->Machine Learning
○ Big Data/Deep Learning->Data Science
● What is Data Science
○ Analytics/Statistics/Machine Learning
● Examples of Machine Learning Applications - moved to week 2
● Types of Machine Learning - moved to week 2
● Homework

3. About me
● Contact Info
○ chris@deepersideoflearning.com
○ www.LinkedIn.com/in/ChristopherHimmel
○ www.DeeperSideOfLearning.com
○ (510)207-8298
● Background Interview
○ Check out Humans of Data Science (Kate Strachnyi)
■ https://www.youtube.com/watch?v=LB0uiADf8PY
■ http://storybydata.com/humans-of-data-science-hods/
● Data Science Dream Job
○ Kyle McKiou and Randy Lau
○ DataScienceDreamJob.com
● Extra(-extra-)curricular
○ Ex-ultramarathoner
○ Distance swimmer
○ Underwater Hockey!

4. Logistics
Student expectations
● Student prerequesites
○ Basic math
○ PC/Mac/Linux
○ Minimal programming experience
○ Excited to learn!
● Community
○ Github portfolio for code sharing
○ LInkedIn for Professional sharing
● Following along with Class Notebook
● Homework assignments
● Final project
○ Proposal
○ Final submission

5. Logistics
Lecture Schedule
● Frequency
○ Tonight until just before Christmas
○ 13 lectures in total
● Monetary
○ First three lectures free for all
○ Next 10 lectures:
■ $20usd/lecture, or
■ $150usd for all
○ Lecture Series free to DSDJ and CPWM groups
● 1 to 1.5 hours per lecture
● Recorded, available at DeeperSideOfLearning.com/recordings

6. Logistics
High Level Course Topics
● Part 1: Introduction to Data Science
○ Purpose
○ Introduction to Neural Networks
○ Basic Mathematics for ML
○ Computing for Analytics
○ Computing for Machine Learning
● Part 2: Deeper Dive
○ Statistical Methods of Machine Learning
○ Neural Network Applications
○ Neural Network Algorithms
○ Project Applications

7. What is “Artificial Intelligence”
● “Mimicking Human Intelligence in Computers” - Turing (1950)
● Traditional Computing
○ Transactional - defined step by step
○ Predetermined pattern/procedures/process
○ Specialized distinct code
○ Digital - 1’s or 0’s, On or Off
● Mimic Human Brain
○ Massively Parallel
○ Analog
○ Estimate
○ Common architecture

8. History
Periods of Artificial Intelligence
• 70’s to 95: early Artificial Intelligence
• 10 years, Dark Ages
• 2005 to 2012: progress, using faster technology
• Hinton birthed Deep Learning (e.g. CNN for Images)
• 2008 Patil, Hammerbacher coined Data Scientist
• 2012 to now: recent explosion of Machine Learning

9. History
Timeline of Artificial Intelligence

10. History
AI Concepts
● Other Forms
○ Fuzzy Logic
○ Genetic Algorithms
○ Statistical Methods
● Neural Network
○ Perceptron
○ Backpropagation
○ Multilayer Perceptron
○ Convolutional Neural Networks
○ Deep Learning
○ Recurrent Neural Networks
● https://www.kdnuggets.com/2018/03/weird-introduction-deep-learning.html
● https://chatbotnewsdaily.com/since-the-initial-standpoint-of-science-technology-and-
ai-scientists-following-blaise-pascal-and-804ac13d8151

11. History
Evolution of Terms
Softening of terms:
● “Artificial”
○ became more defined, less abstract virtual
Only a “Machine”
● “Intelligence”
○ became more defined specific
○ doesn’t include other half of intelligence, “knowledge”
Only “Learning”

12. Data Science
Intersection of Disciplines

14. Data Science Growth

15. What is Data Science
Activities of a Data Scientist
● Engineering
○ Data Cleansing
All
○ Model/Algorithm Programming (Library Creation) Few***
○ Increase processing speed Few
○ Pipeline development
All
● Mathematics/Statistics
○ Data Exploration/Feature Engineering All
○ Model Verification/Comparison All
○ Model/Algorithm Creation Few
● Domain Knowledge
○ Problem Definition
All
○ Visualization

16. Engineering - Data Preparation

17. Lecture 1
Homework
● Fill out your row on Class Google Sheet
● Creat Github account, install Git, learn basics:
○ https://guides.github.com/
○ https://www.codementor.io/git/tutorial/git-github-tutorial-for-beginners
○ https://help.github.com/categories/bootcamp/
● Set up LinkedIn profile if you haven’t already
● Load Python 3 on your laptop (https://www.python.org/downloads/)
● Python intro
○ https://www.learnpython.org/ (interactive Python)
○ https://www.udemy.com/python-basic-level/ (5.5 hours)