The document provides an introduction to artificial intelligence and machine learning. It defines AI as the study of intelligent agents that act rationally to achieve goals. Machine learning is described as a subset of AI that allows systems to learn from data and experience. The relationship between AI, machine learning, and data science is explored, with data science helping to evaluate data and machine learning applying various techniques to that data. A wide range of applications of machine learning are also discussed.

1. Fundamentals of Artificial Intelligence and Machine
Learning
Lecture 1: Introduction to AI and Machine Learning

2. Data Economy

3. Emergence of Artificial Intelligence
Science associated with data is going toward a new paradigm where one can
teach machines to learn from data and derive a variety of useful insights
giving rise to Artificial Intelligence.

4. Prerequisites
Prior knowledge of the following domains and technology is helpful:

5. What is AI?
A system that
think like humans
A systems that
think rationally
A systems that
act like humans
A systems that
act rationally

6. A System that Think like Humans
“The exciting new effort to make computers think …
machines with minds, in the full and literal sense.”
(Haugeland, 1985)
“The automation of activities that we associate with human
thinking, activities such as decision-making, problem solving,
learning…” (Bellman, 1978)

7. A System that Think Rationally
“The study of mental faculties through the use of
computational models.”(Charniak and McDermott, 1985)
“The study of the computations that make it possible to
perceive, reason, and act.” (Winston, 1972)

8. A System that Act like Humans
“The art of creating machines that perform functions that
require intelligence when performed by people” (Kurzweil, 1990)
“The study of how to make computers do things at which, at
the moment, people are better" (Rich and Knight, 1991)

9. A System that Act Rationally
“The branch of computer science that is concerned with the
automation of intelligent behavior.”
(Luger and Stubblefield, 1993)
“Computational intelligence is the study of the design of
intelligent agents.” (Poole et al., 1998)
“AI… is concerned with intelligent behavior in artifacts.”
(Nilsson, 1998)

10. Acting Humanly: the Turing Test
In 1950, Turing predicted that by 2000, a machine
might have a 30% chance of fooling a lay person for 5
minutes
Anticipated all major arguments against AI in
following 50 years
Suggested major components of AI: knowledge,
reasoning, language understanding, learning.
Problem: Turing test is not reproducible,
constructive, or amenable to mathematical analysis.

11. More Definition…
Artificial intelligence is the synthesis and analysis of computational agents that
act intelligently.
An agent is something that acts in an environment.
An agent acts intelligently if:
 its actions are appropriate for its goals and circumstances
 it is flexible to changing environments and goals
 it learns from experience
 it makes appropriate choices given perceptual and computational
limitations

12. Weak and Strong AI Claims
Weak AI: Machines can be made to act as if they were intelligent. The
principle value of the computer in the study of the mind is that it gives us a
very powerful tool.
Strong AI: Machines that act intelligently have real, conscious minds. The
computer is not merely a tool in the study of the mind; rather, the
appropriately programmed computer really is a mind, in the sense that
computers given the right programs can be literally said to understand and
have other cognitive states.

13. Goals of Artificial Intelligence
Scientific Goal: to understand the principles that make
intelligent behavior possible in natural or artificial systems.
 analyze natural and artificial agents
 formulate and test hypotheses about what it takes to construct
intelligent agents
 design, build, and experiment with computational systems that
perform tasks that require intelligence
Engineering goal: design useful, intelligent artifacts.
Analogy between studying flying machines and thinking
machines.

14. Agents acting in an environment: inputs and output

15. Inputs to an agent
Abilities: the set of possible actions it can perform
Goals/Preferences: what it wants, its desires, its values,...
Prior Knowledge: what it comes into being knowing, what it
doesn't get from experience,...
History of stimuli:
 (Current) stimuli: what it receives from environment now
(observations, percepts)
 past experiences: what it has received in the past

16. Example agent: autonomous car
• abilities: steer, accelerate, brake
• goals: safety, get to destination, timeliness . . .
• prior knowledge: street maps, what signs mean, what
to stop for . . .
• stimuli: vision, laser, GPS, voice commands . . .
• past experiences: how breaking and steering affects
direction and speed. . .

17. Example agent: robot
• abilities: movement, grippers, speech, facial expressions,. . .
• goals: deliver food, rescue people, score goals, explore,. . .
• prior knowledge: what is important feature, categories of
objects, what a sensor tell us,. . .
• stimuli: vision, sonar, sound, speech recognition, gesture
recognition,. . .
• past experiences: effect of steering, slipperiness, how people
move,. . .

18. Application of AI
AI is redefining industries by providing greater personalization to users
and automating processes.

19. Data Facilitates in Recommendations
Amazon collects data from users and recommends the best products
according to the user's buying/shopping pattern.

20. Relationship between AI, ML, and Data Science
Even though the terms data science, machine learning, and artificial intelligence
(AI) fall in the same domain and are connected to each other, they have their
specific applications and meaning.

21. Relationship between Artificial Intelligence and Machine
Learning

22. What is Machine Learning?
 The capability of Artificial Intelligence systems to learn by extracting
patterns from data is known as Machine Learning.
 Arthur Samuel (1959): “The field of study that gives computers the
ability to learn without being explicitly programmed."
 Tom Michel (1999): A computer program is said to learn from
experience E with respect to some class of tasks T and performance
measure P, if its performance at tasks in T, as measured by P, improves
with experience E."
Example: playing checkers.
• E = the experience of playing many games of checkers
• T = the task of playing checkers.
• P = the probability that the program will win the next game.

23. Features of Machine Learning

24. Machine Learning Approach
Traditional Approach vs. Machine Learning Approach

25. Traditional Approach
Traditional programming relies on hard-coded rules.

26. Machine Learning Approach
Machine Learning relies on learning patterns based on sample data.

27. Relationship between Machine Learning and Data Science
Data Science and Machine Learning go hand in hand. Data Science
helps evaluate data for Machine Learning algorithms.

28. Relationship between Machine Learning and Data Science

29. Machine Learning Techniques
Machine Learning uses a number of theories and techniques from Data
Science:

31. Machine Learning Techniques

32. Machine Learning Techniques

33. Machine Learning Techniques

34. Machine Learning Techniques

35. Machine Learning Techniques

37. Applications of Machine Learning
Artificial intelligence and Machine learning are being increasingly used
in various functions such as:

38. Applications of Machine Learning

39. Applications of Machine Learning

40. Applications of Machine Learning

41. Applications of Machine Learning

42. Applications of Machine Learning

43. Applications of Machine Learning