CS205: Building with Artificial Intelligence

Defining the term "intelligence" is surprisingly difficult. Alan Turing was among the first computer scientists to suggest what is now called the Turing Test to determine whether some software is exhibiting intelligence. This test has evolved as technology has evolved, but it stands up surprisingly well in capturing the key capabilities associated with intelligence even today. This unit describes the details of the Turing Test and the Full Turing Test and illustrates the concepts with many examples. You will also be provided with an overview of how scientists of different disciplines approach "intelligence". The key question is whether intelligence is defined by something innate or important within the agent being evaluated or more about whether the agent delivers rationally more effective outcomes to users, regardless of how it works internally.

Completing this unit should take you approximately 2 hours.

Modern AI is based on the building block concept of "agents". An agent is typically a software entity that somehow encapsulates intelligent behavior utilizing different capabilities. In this unit, you will learn about the different types of agents with different capabilities to assist users. You will also see how the agent paradigm provides a uniform framework to describe simple and sophisticated agents. Different agents are typically needed to solve problems in different environments. This unit describes the critically important technical properties of environments and will show you how to analyze new problems in those environments. Once you know which kind of environment is involved, specific agents are designed to meet the requirements of those environments.

Completing this unit should take you approximately 2 hours.

A big part of AI is machine learning (ML) – the ability to see patterns in existing data sets and leverage those through advanced algorithms to make better decisions in complex environments. ML has myriad applications and is responsible for many of the apps all of us know and use every day.

This unit describes the various types of ML models: supervised, unsupervised, and reinforcement learning. You will learn how these models are different and how they can be leveraged in creating advanced decision-making systems, with many examples of applications utilizing these methods to advantage.

Completing this unit should take you approximately 3 hours.

In this unit, we will explore the full range of key ML algorithms. We will delve into important classification and regression algorithms and the key distinctions between these two kinds of supervised ML. The unit covers representative classification and regression algorithms and how to assess their performance in predicting new inputs. You will also get an overview of how unsupervised learning, reinforcement learning, and neural networks are used in modern AI applications.

Completing this unit should take you approximately 10 hours.

Problem-solving using AI includes both ML and other paradigms. This unit provides you with a foundation for problem-solving using ML methods, which always requires the availability of data from which to generalize. AI also provides us with other concepts, such as a general problem-solver (GPS), to solve problems more broadly. AI problems come in many flavors, and you can leverage the right algorithm to solve them based on their characteristics.

Completing this unit should take you approximately 5 hours.

Search algorithms play a huge role in AI. GPS, for example, is all about searching for some solution or optimal solutions in a large solution space, often described as a tree. This unit will discuss various search algorithms that can be utilized in many different situations.

Completing this unit should take you approximately 4 hours.

In many problems, it is easy to propose some approximate solution to the problem. It may be inadequate or incorrect or correct but sub-optimal. In such cases, algorithms exist to improve a bad solution to arrive at a better solution. These are called iterative improvement algorithms and are very useful for certain problems. In this unit, we will introduce the concepts of iterative improvements and a range of algorithms that can be leveraged to solve problems in this category.

Completing this unit should take you approximately 4 hours.

Games have become an important component of AI and are a global industry. This unit shows you how to analyze the game state and represent it in a way that fosters interesting algorithms. We will look at a range of algorithms and give an overview of how games can be modeled in AI applications.

Completing this unit should take you approximately 4 hours.

This unit will delve into the full gamut of concepts and methods of natural language processing (NLP) and how it affects modern AI applications. The topics we will cover include what makes NLP hard, the grammatical structures of NLP, and various models, such as long short-term memory networks (LSTM), that have proved valuable in applications. Generative AI, such as ChatGPT, has become wildly popular.

Completing this unit should take you approximately 6 hours.

Reasoning is one of the important core capabilities mentioned in the Turing test for intelligence. Reasoning allows an agent to infer or deduce new information from information already known to be true. Reasoning occurs in different "logics": languages of varying power and other constructs to describe the known characteristics, which include Boolean or propositional logic and first-order logic (FOL). FOL is better capable of compactly describing known information about a system. This unit will help you understand these logics and how inference is carried out in propositional logic and FOL. Finally, we will look at uncertainty models such as Bayesian analysis, Bayesian networks, and Markov Chains, which have proved better than logic-based systems in modeling uncertainty and predicting probabilistic outcomes.

Completing this unit should take you approximately 8 hours.

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Take this exam if you want to earn a free Course Completion Certificate.

To receive a free Course Completion Certificate, you will need to earn a grade of 70% or higher on this final exam. Your grade for the exam will be calculated as soon as you complete it. If you do not pass the exam on your first try, you can take it again as many times as you want, with a 7-day waiting period between each attempt.

Once you pass this final exam, you will be awarded a free Course Completion Certificate.