US MS Artificial Intelligence: Machine/Deep Learning

The basic concept of machine learning in data science involves using statistical learning and optimization methods that let computers analyze datasets and identify patterns. Machine learning techniques leverage data mining to identify historic trends and inform future models.

The typical supervised machine learning algorithm consists of roughly three components:

1. A decision process: A recipe of calculations or other steps that takes in the data and “guesses” what kind of pattern your algorithm is looking to find.
2. An error function: A method of measuring how good the guess was by comparing it to known examples (when they are available). Did the decision process get it right? If not, how do you quantify “how bad” the miss was?
3. An updating or optimization process: A method in which the algorithm looks at the miss and then updates how the decision process comes to the final decision, so next time the miss won’t be as great.

For example, if you’re building a movie recommendation system, you can provide information about yourself and your watch history as input. The algorithm will take that input and learn how to return an accurate output: movies you will enjoy. Some inputs could be movies you watched and rated highly, the percentage of movies you’ve seen that are comedies, or how many movies feature a particular actor. The algorithm’s job is to find these parameters and assign weights to them. If the algorithm gets it right, the weights it used stay the same. If it gets a movie wrong, the weights that led to the wrong decision get turned down so it doesn’t make that kind of mistake again.

Since a machine learning algorithm updates autonomously, the analytical accuracy improves with each run as it teaches itself from the data it analyzes. This iterative nature of learning is both unique and valuable because it occurs without human intervention — empowering the algorithm to uncover hidden insights without being specifically programmed to do so. 

Source: https://www.ibm.com/topics/machine-learning

Source:  https://www.coursera.org/articles/ai-vs-deep-learning-vs-machine-learning-beginners-guide

Deep learning is a subset of machine learning, which is essentially a neural network with three or more layers. These neural networks attempt to simulate the behavior of the human brain—albeit far from matching its ability—allowing it to “learn” from large amounts of data. While a neural network with a single layer can still make approximate predictions, additional hidden layers can help to optimize and refine for accuracy.

Deep learning drives many artificial intelligence (AI) applications and services that improve automation, performing analytical and physical tasks without human intervention. Deep learning technology lies behind everyday products and services (such as digital assistants, voice-enabled TV remotes, and credit card fraud detection) as well as emerging technologies (such as self-driving cars).

Source: https://www.ibm.com/topics/deep-learning