Artificial Intelligence: Optimization Algorithms in Python

Artificial Intelligence: Optimization Algorithms in Python

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