English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 6.5 Hours | 3.38 GB

Learn how to build optimization algorithms from the ground up!

What would an “optimal world” look like to you? Would people get along better? Would transport run faster? Would we take better care of our environment?

Many data scientists choose to optimize by using pre-built machine learning libraries. But we think that this kind of ‘plug-and-play’ study hinders your learning. That’s why this course gets you to build an optimization algorithm from the ground up.

In Artificial Intelligence: Optimization Algorithms in Python, you’ll get to learn all the logic and math behind optimization algorithms. With two highly practical case studies, you’ll also find out how to apply them to solve real-world problems.

In the first case study, we’ll optimize travel plans for six friends who want to fly out from the same airport. In the second case study, we’ll optimize the way university administrators allocate dorm rooms to new students.

On the way, we’ll learn what optimization algorithms are. We’ll find out how they can be applied to daily business practice. And we’ll see how they can learn by themselves.

This course introduces you to four types of optimization algorithms:

- random search
- hill climb
- simulated annealing, and
- genetic

Don’t worry if you’re not yet sure what any of these are. We’ll go through each one in detail, and you’ll find out how to build each of them in our two case studies.”

What you’ll learn

- Learn the theory and implement optimization algorithms from scratch for solving real problems
- Implement step by step the following algorithms in Python: random search, hill climb, simulated annealing, and genetic algorithms
- Solve real problems for optimising flight calendars and dormitory room optimisation (limited resources)
- Implement optimisation algorithms using predefined libraries

**+ Table of Contents**

**Introduction**

1 Introduction and course content

2 Applications of optimization algorithms

3 Source code and slides

**Representation of AI problems – group travel**

4 Plan of attack

5 Case study

6 Creating the variables

7 Flights dataset

8 Printing the flights schedule – implementation

9 Installing Anaconda and PyCharm

10 Printing the flights schedule – debug

11 Hours to minutes – implementation

12 Fitness function – implementation 1

13 Fitness function – implementation 2

14 Fitness function – debug

**Random search**

15 Plan of attack

16 Implementation

17 Debug

**Hill climb**

18 Plan of attack

19 Theory

20 Implementation

21 Debug

22 Homework instruction

23 Homework solution

24 Additional reading

**Simulated annealing**

25 Plan of attack

26 Theory

27 Implementation

28 Debug

29 Homework instruction

30 Homework solution

31 Additional reading

**Genetic algorithm**

32 Plan of attack

33 Theory

34 Implementation 1 – mutation

35 Implementation 2 – crossover

36 Implementation 3 – genetic algorithm

37 Debug

38 Homework instruction

39 Homework solution

40 Comparing the results

41 Additional reading

**Limited resources – bedrooms problem**

42 Plan of attack

43 Case study

44 Defining the domain

45 Printing the solution

46 Fitness function

47 Optimization algorithms

48 Comparing the results

**Maximizing profit – transport of products**

49 Plan of attack

50 Case study

51 Domain and printing the solution

52 Fitness function

53 Optimization algorithms

54 Comparing the results

**Library for optimization algorithms**

55 Plan of attack

56 MLROSe library 1

57 MLROSe library 2

58 Homework instruction

59 Homework solution

**Final remarks**

60 Final remarks

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