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Block 1: The map: from asking the data to commissioning an agent
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Three questions to the data and what changes with AI
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The team, the agent and the six-verb map
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Block 1 exam
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Block 2: Data: the raw material
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What shape a data point has
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Where data comes from and whose it is
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Block 2 exam
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Block 3: Uncertainty: probability so you don't believe everything
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Probability: spreading belief across what can happen
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Bayes and false alarms
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Relationship, chance and cause
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Block 3 exam
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Block 4: Learning from data: what training means
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The smallest model: a line with two knobs
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From the line to the neuron
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The families of learning and the map of techniques
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Block 4 exam
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Block 5: Generalizing and evaluating: how to know if it works
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Memorizing is not learning
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Hits, misses and what each one costs
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Reading an evaluation without being fooled
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Block 5 exam
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Block 6: Language models: a text-prediction machine
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From text to vectors
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How it generates: context and the next word
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Why it is wrong with such confidence
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Block 6 exam
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Block 7: Agents: from answering to acting
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What an agent is: a model, tools and a loop
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Giving it context and briefing it well
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Reading what it has done and keeping a record
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Block 7 exam
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Block 8: Person and machine: dividing, supervising and answering for the work
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What gets delegated and what does not
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Supervising without nodding off and without losing the craft
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Answering for what you sign
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Block 8 exam
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