Curated Information from different Sources on Artificial Intelligence, Deep Learning, Machine Learning, Predictive Analytics, Neural Networks, Anomaly Detection, Novelty Detection

1. A journey into AI, ML and DL
Sampath, Rangaprasad ranga.sampath@gmail.com
S, Madhusoodhana Chari madhucharis@gmail.com

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4. AI experiments
Source: Jason’s ML 101

5. Source: Jason’s ML 101

6. Source: Jason’s ML 101

7. Source: Jason’s ML 101

8. Source: What AI means for our future?

10. Source: Jason’s ML 101

11. Source: George Seif

12. https://www.slideshare.net/LuMa921/deep-learning-the-past-present-and-future-of-artificial-intelligence

13. Q & A

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19. Class Visibility
Unsupervised
Pattern Variations
SEMI SUPERVISED
COMPENSATE
MISSING SAMPLES
Information
Similarity
Probability
ERROR

21. Consistent Generalize
Excessively Simple
Low Variance High Bias
cannot capture the underlying
trend
Underfit
Does not fit the data well enough
Excessively Complicated
Low Bias High Variance
captures the noise
Overfit
fits data too well

22. D’CANA - DECISION TREE CLASSIFICATION ADDENDUM
FOR NUMERICAL ATTRIBUTES
What if the following samples are seen in test data?
• A whale of length 7 m, which is actually a Killer Whale.
• .
• A whale of length 3 m, which is actually a Beluga Whale.
Data Segmentation
Seen bands – [4,5], [15-16], [25-26]
Unseen bands – [<=3], [6-14], [17-24], [>26]

23. Source: Josh Bloom

26. Q & A

27. THANK YOU