The document discusses machine learning (ML) and artificial intelligence (AI), highlighting their applications in various fields, including email categorization, ridesharing, self-driving cars, and social networking. It emphasizes the distinction between traditional programming and ML, specifically how ML models learn from data to improve performance over time. Additionally, the document mentions deep learning as an advanced ML technique and provides resources for further reading on the subject.

1. Machine Learning
Personal website:
http://intranet.daiict.ac.in/~mv_joshi/

2. AI
 Artificial intelligence refers to theory and
development of computer systems to
perform tasks of human beings: such as
visual perception, speech recognition,
decision making, translation between
languages etc.

3. Traditional Computer Science
Slides:Kilian Weinberger
 Explicitly programmed rules and logic
 Deterministic and predictable outputs
 Data is used as input to pre-defined
algorithms
 Complexity: In algorithmic logic and data
structures.

4. Machine Leaning
 ML Model learns patterns from given data
 Prediction: Probabilistic and depends on
data
 Focus on data collection, model training,
validation, and tuning
 Data is central to the learning process,
used to train models
 Complexity often lies in model selection,
training, and evaluation.

6. What is Machine Learning (ML)
Formal Definition by Mitchell, 1997:
A computer (machine) program A is said
to learn from experience E (more the data
better experience) with respect to some
task T and performance measure P if it’s
performance improves with E
Informally: Algorithms that improve on
task performance with experience.

7. AI/ML applications – In use
 Your email: Separate Spam mails, email
categorization, smart reply – Gmail
 Ridesharing Apps Like Uber – How they
determine price? Optimally match you
with other passengers to minimize long
route
 Commercial flights: AI Autopilot mode
 Self driving cars
 Plagiarism check

8.  Banking: Check deposit (OCR - Mitek), Fraud
prevention
 Investment advice (Wealthfront, Betterment)
 Social Networking: Facebook: automatically
highlighting faces and tagging friends – face
recognition
 Instagram:
 Snapchat
 Online shopping: Amazon
 Voice to text: Alexa

9. Deep learning
 Deep Learning is a new area of
Machine Learning research, which
has been introduced with the
objective of moving Machine
Learning closer to one of its
original goals: Artificial
Intelligence.”

10. Readings
 1. Andrew Ng Notes
 2. Lectures by Ali Ghodsi, University of
Watrloo
 3. Lectures by Kilian Weinberger, Cornell
University
 Books on Machine learning
1. Kevin Murphy Machiine Learning A Probabilistic Perspective, MIT
2. Hastie, Tibshirani, Friedman The Elements of Statistical Learning,
Springer.

11. Visit to see the inspirational applications!
 http://machinelearningmastery.com/
inspirational-applications-deep-learning/
Jason Brownlee on July 14, 2016 in Deep Learning

12. Colorization of black and white movies

13. Adding sounds to silent movies
 System must synthesize sounds to match
a silent video.

14. Autometic translation of text

15. Object detection

16. Automatic image caption generation

18. ICA (Demo) Andrew Ng Slides
https://cnl.salk.edu/~tewon/Blind/
blind_audio.html