See how a combination of labeled and unlabeled examples can be exploited in machine learning, specifically by using clustering to learn about the data before making use of the labeled examples.

Recommender systems are ubiquitous, from book and movie tips to work aids for professionals. But how do they function? Look at three different approaches to this problem, focusing on Professor Littman's dilemma as an expert reviewer for conference paper submissions, numbering in the thousands. Also, probe Netflix's celebrated one-million-dollar prize for an improved recommender algorithm.

In 1959, computer pioneer Arthur Samuel popularized the term "machine learning" for his checkers-playing program. Delve into strategies for the board game Othello as you investigate today's sophisticated algorithms for improving play, at least for the machine. Also explore game-playing tactics for chess, Jeopardy!, poker, and Go, which have been a hotbed for machine-learning research.

Discover how the ImageNet challenge helped revive the field of neural networks through a technique called deep learning, which is ideal for tasks such as computer vision. Consider the problem of image recognition and the steps deep learning takes to solve it. Dr. Littman throws out his own challenge: Train a computer to distinguish foot files from cheese graters.

Roll up your sleeves and debug a deep-learning program. The software is a neural net classifier designed to separate pictures of animals and bugs. In this case, fix the bugs in the code to find the bugs in the images! Professor Littman walks you through diagnostic steps relating to the representational space, the loss function, and the optimizer.

One way to think about the creative process is as a two-stage operation, involving an idea generator and a discriminator. Study two approaches to image generation using machine learning. In the first, a target image of a pig serves as the discriminator. In the second, the discriminator is programmed to recognize the general characteristics of a pig, which is more how people recognize objects.

A new approach to image generation and discrimination pits both processes against each other in a "generative adversarial network," or GAN. The technique can produce a new image based on a reference class, for example making a person look older or younger, or automatically filling in a landscape after a building has been removed. GANs have great potential for creativity and, unfortunately, fraud.

Are you no good at programming? Machine learning can a give a demonstration, predict what you want, and suggest improvements. For example, inverse reinforcement turns the tables on the following logical relation, "if you are a horse and like carrots, go to the carrot." Inverse reinforcement looks at it like this: "if you see a horse go to the carrot, it might be because the horse likes carrots."

Get acquainted with a powerful new tool in machine learning, causal inference, which addresses a key limitation of classical methods: the focus on correlation to the exclusion of causation. Practice with a historic problem of causation: the link between cigarette smoking and cancer, which will always be obscured by confounding factors. Also look at other cases of correlation versus causation.

Probe the deep-learning revolution that took place around 2015, conquering worries about overfitting data due to the use of too many parameters. Dr. Littman sets the stage by taking you back to his undergraduate psychology class, taught by one of The Great Courses' original professors. Chart the breakthrough that paved the way for deep networks that can tackle hard, real-world learning problems.

Machine learning is both a cause and a cure for privacy concerns. Hear about two notorious cases where de-identified data was unmasked. Then, step into the role of a computer security analyst, evaluating different threats, including pattern recognition and compromised medical records. Discover how to think like a digital snoop and evaluate different strategies for thwarting an attack.

Finish the series with a lightning tour of meta-learning: algorithms that learn how to learn, making it possible to solve problems that are otherwise unmanageable. Examine two approaches: one that reasons about discrete problems using satisfiability solvers and another that allows programmers to optimize continuous models. Close with a glimpse of the future for this astounding