计算机视觉代写 | Intro to Machine Learning
本次美国代写是关于Python计算机视觉相关的assignment
• Previously: we assume knowledge about the world is given
• Automated discovery of patterns in data and acting on it
• Data → pattern → model → action/decision
• Model: a simplification of some phenomenon
• MDP: transition function
• Sensor stochasticity: Bayes net
Data analytics
Making decisions
Finding patterns in data
• In Machine Learning we no longer make the assumption that models are given
• Too complicated
• Too many factors – don’t know what is important
• Model is not static – needs to be regularly updated
• Instead: give the agent a bunch of data about the phenomena and let the agent reverse-engineer a model
Types of Learning
• Supervised learning
• Give correct answer for each instance
• Learn a function from examples of inputs/outputs
• Take away the supervision and interpolate to new instances
Example data: inputi Expected_outputi
F(input) Predicted_outputi
Check expected_outputagainst predicted_ouput
If they differ, update f()
Eat at a restaurant?
• Features (observables):
• Alternate: suitable alternate restaurant nearby (y/n
• Bar: A bar to wait in (y/n)
• Fri/Sat: it’s a Friday or Saturday (y/n)
• Hungry: y/n
• Price: price range ($, $$, $$$)
• Raining: y/n
• Reservation: we made a reservation (y/n)
• Type: french, italian, thai, burger
• WaitEstimate: 0-10, 10-30, 30-60, >60
• Patrons: none, some, full
Training set
Supervised learning is function approximation