1) The document discusses artificial intelligence and machine learning, including different types of machine learning problems like supervised learning, unsupervised learning, and reinforcement learning. 2) It provides examples of applications of these machine learning techniques, such as spam filtering for classification, clustering similar news items for unsupervised learning, and AlphaGo for reinforcement learning. 3) Deep learning and artificial neural networks are described as techniques that use a multi-level network approach to capture abstractions in data without requiring feature engineering.

1. Artificial
intelligence
Machine learning

2. Lee Sedol vs. AlphaGo
Silver, David, et al. "Mastering the game of Go with deep neural networks and tree search." Nature (2016)

3. “At least a decade to go before a computer can beat
a human expert”
Not very long AGo!

4. 1000 = 103
100000000000000000000000000000000000000000000000 = 1047
10000000000000000000000000000000000000000000000000
00000000000000000000000000000000000000000000000000
00000000000000000000000000000000000000000000000000
000000000000000000000 = 10170
Why this assessment?

5. What is AI?
https://xkcd.com/329/

6. AI Winter
Series of setbacks from 70s till 90s
High Expectations
Failure of LISP machines
Failure of expert systems

7. AI Spring
IMAGE: GETTY IMAGES/ISTOCKPHOTO

8. AI Spring

9. AI Spring
Domingos, Pedro. "A few useful things to know about machine learning." Communications of
the ACM 55.10 (2012)

10. What is ML?
“Field of study that gives computers the ability to
learn without being explicitly programmed”

11. Types of ML problems
Machine learning
Unsupervised
learning
Supervised
learning
Reinforcement
learning
Regression Classification

12. Supervised Learning
Spoonfeeding labelled examples
Numerical values or Discrete class
labels
Machine has to be ‘trained’ using a
large corpus of ‘training data’

13. Regression
Training
data
Hypothesis
Choosing optimum ‘hypothesis’ from training data
Hypothesis chosen has minimum ‘cost’
Typically used in financial applications, like predicting stock prices or likely
monetary value of products

14. Classification
Height
Width
Decision
boundary
Finding decision boundaries
based on the labels of the
training data
Non-linear decision
boundaries require complex
classifiers like SVMs and
neural nets

15. Classification applications
Spam filtering
Optical Character Recognition
Pedestrian detection

16. Unsupervised Learning (Clustering)
Training data is not labelled
Grouping based on density (DBSCAN,
OPTICS), cluster centers (K-Means)
or probability distribution (GMM)

17. Clustering applications
Grouping similar news items Kharinov, M. "Hierarchical pixel clustering for image segmentation." arXiv preprint (2014).
Pixel clustering for segmentation

18. Reinforcement Learning
Teaching a machine by ‘rewarding’ it
for good ‘actions’ and ‘punishing’ it
for bad ones
Attempt is to explore the entire state
space for a problem and get the best
actions corresponding to each state,
also known as ‘policy’

19. Reinforcement Learning applications
Reinforcement learning used for AlphaGo

20. Deep Learning
Capturing abstractions using a multi-
level or ‘network’ approach
Each level or ‘layer’ composed of many
simple processing units
The internal abstractions are often the
best features to use for the problem,
so no feature engineering is required

21. Artificial Neural Networks (ANNs)
Deep networks composed of artificial
neurons
Inspired by biological neurons
Activation function is typically
sigmoid, can be tanh or ReLu
The method used to train a network is
called ‘backpropagation’
Traditional neural networks with all
signals propagating in one direction
are called ‘feedforward’ networks
Structure of a typical biological neuron
Typical artificial neuron

22. Artificial Neural Networks (ANNs) contd.
Rectifier function
Logistic function

23. Artificial Neural Networks (ANNs) contd.
Sigmoid function Typical feedforward neural network

24. Recurrent Neural Networks (RNNs)
Hidden layers feed back into
themselves
Can be used to model sequences and
for use as associative memory
Can take input sequences of arbitrary
length using the concept of
‘attention’

25. RNN applications (with links)
Automatic music generation (Site has source code link)
Handwriting synthesis (Site has paper and source code links)
Intelligent personal assistants like Siri, Google Now, Cortana
Automatic image captioning
Sunspring
LSTM that generates poems

26. Learning Resources
Good courses or tutorials for ML
Coursera ML by Andrew Ng
Datacamp ML course
Udacity Deep Learning
Learning by doing
Kaggle
Topcoder Data science
Good video lectures for ML
Gilbert Strang lectures on Linear Algebra
Nando de Freitas Deep Learning
Some people I follow in ML
Andrej Karpathy Peter Norvig
Alex Graves Fei Fei Li
Andrew Ng
Some good blogs on ML
WildML
IAmTrask
Karpathy’s blog
And finally there’s Google
Scholar. Read lots of
research papers and try to
implement them!

27. Thank You
Happy Learning :D