Chapter 1: What is ML: Why the hype right now. (30 pages)
a) Conversation with a Machine Learning Expert
b) Where’s ML being used today?
i) Spam checking, spell check and grammar
ii) Siri, search engines, music selection (Spotify…)
c) Short history of Machine learning dating back to the 1950s
d) What is AI and what’s its relationship to ML
e) Why Machine Learning is Hot Right now
i) Storage is more accessible than ever
ii) Access to compute, especially GPUS, is higher than ever
iii) New algorithms are being created every day
iv) New tooling making things more accessible
f) How is Machine Learning done?
i) What’s inside a model?
ii) What’s a feature?
g) How do data scientists think about feature extraction?
i) This requires domain expertise so a data scientist in the financial space, for example, wouldn't necessarily be effective in machine translation tasks or obstacle avoidance.
h) What are some of the new tools that will help folks access machine learning
Chapter 2: What is DL, how does it differ, why now? (20 pages)
a) Conversation involving Deep Learning
b) Deep learning is a branch of machine learning.
i) Machines do their own feature extraction
ii) Deep learning has turned intractable problems to tractable
c) What’s made this possible at this point?
i) Compute, especially GPUs, are more accessible than ever
ii) New Math: Back propagation and Gradient Descent
d) Constructing a deep network
i) Training a model
ii) Using activation functions
iii) batch normalization.
iv) What is dropout?
v) Choosing an optimization function: SGD and Beyond
vi) Evaluating a model
vii) What is a SoftMax?
Chapter 3: Things that kind of look like AI and solve amazing problems but really aren't... (20 pages)
a) Conversation between a programmer and an ML expert on choosing the right tool for the job
b) Why are these things not actually Machine Learning?
i) Who programmed the rules?
ii) Deterministic verses Probabilistic results
c) Ways to solve problems that look like machine learning
i) Expert systems – rule based systems including state machines
(1) When to use an expert system
(2) Pitfalls and drawbacks of an expert system
ii) Convex optimization – set of techniques for deterministically finding the optimal resourcing
(1) When to use convex optimization
(2) Pitfalls and drawbacks to convex optimization
iii) Time-series Forecasting - using past data to predict the future, taking into account seasonal effects and short/long-term trends
(1) When to use time-series forecasting
(2) Pitfalls and drawbacks to using time-series forecasting
iv) Dynamic programming - cleverly breaking problems down, solving the easier smaller ones, and storing their solutions
(1) When to use dynamic programming
(2) Pitfalls and drawbacks of dynamic programming
Chapter 4: What sort of problems can you /should you solve with ML? DL? (20 pages)
a) Conversation with data scientist around selecting ML tools
b) What’s ML really good at?
i) Discussion of problems where ML has helped
c) What’s ML not good at?
i) A walk through a few problems where ML doesn't do very well
ii) Not enough data
iii) Curse of dimensionality
d) Recognizing a machine learning problem
i) Filtering out problems that can be solved by methods in Chapter 3
ii) Phrasing your problem as an ML problem
e) When is an ML problem actually a Deep Learning problem? How to know when this answer has changed (things are moving fast!)
Chapter 5: ML: Dealing with Data (featurization) (20 pages)
a) Conversation with a data scientist about featurization of data.
b) How do determine what data you need to collect
c) How to store this data
i) Data store
ii) Formats
d) How a data scientist works with data
i) Data cleaning
ii) Labeling
iii) Featurization
e) Potential pitfalls
i) Feature skew and Heteroskedasticity
ii) Label skew
iii) Interdependence of features
iv) Outlier detection
v) Data sparsity
vi) Missing values
Chapter 6: ML Under Supervision: Regression and Classification (20 pages)
a) The primary two methods of solving problems with classic machine learning
i) Classification turns features into a single decision such as a yes or no or into different buckets. E.G. Will it rain tomorrow?
ii) Regression turns features into numeric values. E.G. What’s the temperature likely to be tomorrow?
Chapter 7: Unsupervised and Semi-supervised ML (20 pages)
a) Conversation with a data scientist about what a computer can do without any direction
b) What unsupervised learning can do
i) Working with unlabeled data
ii) &
