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Andreas Haja, Instructor - Uncertain Knowledge and Reasoning in Artificial Intelligence

Andreas Haja

The instructor is an industry expert for autonomous driving, sensors and computer vision with more than 10 years of professional experience in the automotive space. With Volkswagen, he was a project manager for advanced driver assistance systems and sensor technologies, including cameras, radar and LiDAR. Prior, he worked for Bosch as a computer vision research engineer. In most of his projects, artificial intelligence played a central role. In 2014, the instructor was appointed professor at a university in Northern Germany where he researches and teaches at the faculty of engineering.

Instructor: Andreas Haja

Learn how to take informed decisions based on probabilities and expert knowledge

  • Understand and explore one of the most exciting advances in AI in the last decades.
  • Many hands-on examples, including Python code.
  • Instructor is a professor at the University of Applied Sciences in Emden Germany. He's a technology expert for autonomous driving, driver assistance systems and computer vision with more than 10 years of professional experience.

Duration: 2h 15m

Course Description

From stock investment to autonomous vehicles: Artificial intelligence takes the world by storm. In many industries such as healthcare, transportation or finance, smart algorithms have become an everyday reality. To be successful now and in the future, companies need skilled professionals to understand and apply the powerful tools offered by AI. This course will help you to achieve that goal. This practical guide offers a comprehensive overview of the most relevant AI tools for reasoning under uncertainty. We will take a hands-on approach interlaced with many examples, putting emphasis on easy understanding rather than on mathematical formalities. After this course, you will be able to... … understand different types of probabilities … use Bayes’ Rule as a problem-solving tool … leverage Python to directly apply the theories to practical problems … construct Bayesian networks to model complex decision problems … use Bayesian networks to perform inference and reasoning Wether you are an executive looking for a thorough overview of the subject, a professional interested in refreshing your knowledge or a student planning on a career into the field of AI, this course will help you to achieve your goals.

What am I going to get from this course?

  • The opportunity to understand and explore one of the most exciting advances in AI in the last decades
  • set of tools to model and process uncertain knowledge about an environment and act on it
  • deep-dive into probabilities, Bayesian networks and inference
  • Many hands-on examples, including Python code
  • firm foundation to further expand your knowledge in AI

Prerequisites and Target Audience

What will students need to know or do before starting this course?

  • This course is structured in a way that it is largely complete in itself.
  • Helpful but not required is familiarity with
    • Linear Algebra, Probability, Statistics
    • Computer Programming, preferably Python

Who should take this course? Who should not?

  • Executives and decision makers looking for a comprehensive overview of the subject
  • Professionals and students with a formal college education in Engineering, science, or mathematics


Module 1: Welcome to this course!

Lecture 1 Introduction & Course Overview

In this lecture, I will introduce you to the course, its main goals and topics as well as its significance in the field of AI. Also, I will briefly introduce myself as your instructor and mentor on this journey.

Lecture 2 Introductory Example: Medical Diagnosis

In this lecture, we will look at an introductory example from the field of medical diagnosis. The goal is to develop a feel for probabilities and for the deceptive properties of human intuition.

Module 2: Quantifying Uncertainty - Probability Theory and Bayes' Rule

Lecture 3 Intelligent Agents

In this lecture, you will learn about the various types of agents in AI and the differences between them. Also, I will introduce the agent type we will be concerned with in this course.

Lecture 4 How to deal with uncertainty

In this lecture, you will learn about the major approaches with which to address uncertainty. You will learn about logic, sentences and models.

Lecture 5 Example "Predicting a Burglary" (logic-based)

In this first example, we will try to predict wether our alarm has been triggered by an earthquake or by an actual burglary.

Lecture 6 Basic probability theory

In this lecture, you will learn that probabilities are an effective way of dealing with gaps in models or in data we observe. Also, I will introduce random variables as a means to build a model of an environment.

Lecture 7 Conditional Probabilities

In this lecture, we look at various types of probability and the differences between them. We will focus on conditional probabilities, which are a prerequisite for understanding Bayesian concepts.

Lecture 8 Bayes’ Rule

In this lecture, I will introduce Bayes' Rule, one of the cornerstones of modern AI. You will learn how this simple rule allows us to reverse the order between what we observe and what we want to know.

Lecture 9 Example: "Pedestrian Detection Sensor"

In this example, the reliability of a sensor for detecting pedestrians is assessed using Bayes' Rule. Relying only on its sensors, an autonomous vehicle has to decide wether to issue an emergency breaking or not.

Lecture 10 Example "Clinical Trial" (with Python code)

In this example, we will apply Bayes' Rule to a scenario surrounding a clinical trial. In addition to solving some equations on our own, we will also make use of Python to facilitate computation.

Module 3: Representing uncertainty - Bayesian networks and probability models

Lecture 11 Combining Evidence

In this lecture, you will learn how evidence from multiple sources can be combined to formulate more complex queries. Also, you will learn about the Naive Bayes Model, a concept in AI that works surprisingly well in practice.

Lecture 12 Constructing Bayesian Networks

In this lecture, I will introduce Bayesian networks as a tool to graphically model relationships between multiple conditionally independent random variables.

Lecture 13 Reasoning in a 2-node Network

In this lecture, we will focus on how to update the belief into a random variable by using the law of total probability and Bayes' rule. Also, we will look at how inference is performed in this simple setup.

Lecture 14 Reasoning in a 3-node Network

In this lecture, I will introduce causal, diagnostic and inter-causal inference. Depending on the available evidence and on the direction of reasoning within the network, we will look at how inference is performed in this slightly more complex setup.

Lecture 15 Polytrees and Belief Propagation

In this lecture, we will look at networks where there is at most one path between any pair of nodes. Also, you will learn about a standard algorithm for performing inference called 'belief propagation'.

Lecture 16 Example "Predicting a Burglary" (extended)

In this example, we will expand the burglary scenario by adding more variables and modeling them into a Bayesian network. We will also illustrate the workflow of the message passing algorithm.

Lecture 17 Example "Predicting a Burglary" (in Python)

In this example, I will introduce the Python toolbox 'pgmpy' as a mighty software to model Bayesian networks and answer queries using inference algorithms such as message passing. Finally, I will show how to take decisions based on probability distributions within the network.

Module 4: Summary & final remarks

Lecture 18 Course Summary
Lecture 19 Final Remarks