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Arizona State University

AI and Machine Learning MasterTrack® Certificate

Arizona State University via Coursera MasterTrack

Overview

Gain in-demand skills in artificial intelligence and machine learning by studying statistical machine learning, deep learning, supervised and unsupervised learning, knowledge representation and reasoning from the #1-ranked school for innovation in the U.S.

Learn the theories and techniques used by practitioners in the field of artificial intelligence and machine learning.

In this program, you will complete a real module from the online Master of Computer Science program that will help you understand artificial intelligence through a combination of both theory and practice. Through a series of interactive lectures and team-based projects, you will explore how machines learn in the form of learning paradigms, how to create autonomous agents that can reason, learn, and act on their own, and how to train and optimize deep neural networks.

You’ll also strengthen foundational skills in mathematics that will underpin your work in the field of artificial intelligence. Upon completion of these courses, you will have a strong understanding of the techniques used by practitioners in the field of AI, allowing you to advance your career in AI, work more effectively on machine learning projects, and identify opportunities for how you can apply AI to your current role or company.

By committing to online study for 6-9 months, you can earn the Artificial Intelligence and Machine Learning MasterTrack Certificate that will be a pathway to the online Master of Computer Science degree at Arizona State University.

Syllabus

Course 1: Statistical Machine Learning
- Deriving generalizable models from training data is central to statistical machine learning. Statistical machine learning has found wide applications in many fields including artificial intelligence, computer vision, natural language processing, finance, bioinformatics, and more. This course provides a systematic introduction to common learning paradigms in statistical machine learning, accompanied by an exploration of a set of foundational algorithms. Specific topics covered include: - Mathematical foundations for machine learning - Maximum likelihood estimation - Naive Bayes classification - Logistic regression - Support vector machines - Probabilistic graphical models - Mixture models - K-means clustering - Spectral clustering - Dimensionality reduction - Principal component analysis - Neural networks and deep learning - Convolutional neural networks

Course 2: Artificial Intelligence
- The field of artificial intelligence (AI) develops the principles and processes for designing autonomous agents. This course addresses the core concepts in designing autonomous agents that can reason, learn, and act to achieve user-given objectives and prepares you to address emerging technical and ethical challenges using a principled approach to the field. Specific topics covered include: - Neural Networks - Classical Planning - Modeling & Reasoning - Reinforcement Learning - Markov Decision Processes (MDPs) - Partially Observable Markov Decision Processes (POMDPs) - Bayesian Networks - Sensors for Perception - Perception-based Recognition - Real-world Applications - Robotics

Course 3: Knowledge Representation and Reasoning
- Knowledge representation and reasoning (KRR) is one of the fundamental areas in artificial intelligence. It is concerned with how knowledge can be represented in formal languages and manipulated in an automated way so that computers can make intelligent decisions based on the encoded knowledge. KRR techniques are key drivers of innovation in computer science, and they have led to significant advances in practical applications in a wide range of areas from artificial intelligence to software engineering. In recent years, KRR has also derived challenges from new and emerging fields including the semantic web, computational biology, and the development of software agents. This course introduces fundamental concepts as well as surveys recent research and developments in the field of knowledge representation and reasoning. Specific topics covered include: - Classical logic and knowledge representation - Answer set programming - Reasoning about actions and planning - Ontology, Semantic Web languages, and knowledge graph - Combining logic and probability

Course 4: Intro to Deep Learning in Visual Computing
- In recent years deep learning has revolutionized the field of artificial intelligence. Modern deep neural networks extract patterns in large amounts of data in order to solve very complex real world problems. In this course, you will learn the basic principles of designing and training deep neural networks with a focus on computer vision. You will learn the principles of convolutional neural networks, generative modeling for unsupervised learning, and much more. Specific topics covered include: - Introduction to visual representation & fundamentals of machine learning - Neural networks and backpropagation - Optimization techniques for neural networks - Modern convolutional neural networks - Unsupervised learning and generative models - Transfer learning

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