Slides to the third lecture of my undergraduate course "AI & Society."

1. Machine Learning

2. CAS-HC225
Spring 2024 2/17
AI & Machine Learning
●
Artificial Intelligence represents a broad discipline
aimed at creating machines or computer programs
capable of performing tasks that, if done by humans,
would require some level of intelligence. These tasks
include:
✔
problem-solving,
✔
understanding natural language,
✔
recognizing patterns, and
✔
learning from experience.
●
Machine Learning is a subset of AI focused
specifically on the aspect of learning: Learn and Adapt!
✔
Develop algorithms and statistical models that enable
computers to perform tasks without using explicit
instructions.
✔
Instead, they rely on patterns and inference derived from
data: Give machines access to data and let them learn
for themselves.

3. CAS-HC225
Spring 2024 3/17
Rule-Based AI: Medical Diagnostics
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Knowledge base: Contains detailed information about various medical
conditions and their associated symptoms, diagnostic procedures, and
possible treatments. Developed by medical experts.
●
Rule engine: Applies logical rules to the information input.
✔
IF the patient has a fever AND a sore throat THEN consider the possibility of
strep throat.
●
User Interface: Input patient data (symptoms, medical history, and test
results).
●
Diagnosis and Recommendations: Based on the rules, generate a ranked list
of possible diagnoses along with recommendations for further testing or
treatment options.
●
MYCIN (Stanford, early 1970s), DXplain (MGH, 1984–1986)

4. CAS-HC225
Spring 2024 4/17
Advantages
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Consistency and Speed: Provides consistent assessments and can process
information much faster than a human.
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Accessibility: Makes expert-level knowledge more accessible, especially in
underserved areas.
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Support Decision Making: Acts as a support tool for practitioners,
enhancing their ability to make informed decisions.

5. CAS-HC225
Spring 2024 5/17
Limitations
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Lack of Flexibility: Rule-based systems can only operate within the confines
of their predefined rules and cannot learn from new data or cases outside of
their initial programming.
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Maintenance: The knowledge base and rules need regular updates to
incorporate the latest medical research and findings, which can be resource-
intensive.
●
Accountability: Ethical dilemmas.

6. CAS-HC225
Spring 2024 6/17
Machine Learning Process
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Understanding Data
✔
Gathering relevant data.
✔
Cleaning data: Removing inaccuracies or duplicates.
✔
Labeling data: Classifying into categories.
✔
Feature selection: Choosing relevant data points.
●
Choosing a Model:
✔
Regressions, decision trees, neural networks, support vector machines...
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Learning
✔
Training: The selected model is “trained” using a portion of the data set. During
training, the model adjusts to better fit the data.
✔
Testing: A separate portion of the data, not seen by the model during training, is
used to validate the model's accuracy and generalizability.
●
Making Predictions

7. CAS-HC225
Spring 2024 7/17
Gathering

8. CAS-HC225
Spring 2024 8/17
Cleaning

9. CAS-HC225
Spring 2024 9/17
Labeling

10. CAS-HC225
Spring 2024 10/17
Feature Extraction & Selection

11. CAS-HC225
Spring 2024 11/17
Choosing a Model

12. CAS-HC225
Spring 2024 12/17
Training
encoder
model
features

13. CAS-HC225
Spring 2024 13/17
Supervised vs. Unsupervised
●
Supervised learning:
✔
Supply the labels to the model
✔
Movie recommendation system asks a customer whether they like a
👍
particular movie or not (or asks for a rating)
👎
●
Unsupervised learning:
✔
Do not supply the labels, let the model find the patterns itself
✔
Movie recommendation system analyzes the movies watched by a customer and
attempts to “understand” what the customer likes

14. CAS-HC225
Spring 2024 14/17
Reinforcement Learning
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The model (“an agent”) with little or know prior knowledge “pokes” the
environment
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The environment rewards the agent for “right” choices and penalizes for
“wrong” choices (“carrot and stick”)
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Used in videogames, robotics, algorithmic trading, autonomous vehicles

15. CAS-HC225
Spring 2024 15/17
Testing
encoder
model
features

16. CAS-HC225
Spring 2024 16/17
Predicting
encoder
model
features

17. CAS-HC225
Spring 2024 17/17
What’s Next?
Neural Networks