Artificial Intelligence Course

Artificial intelligence is intelligence that accepts, inputs synchronize, and collects information by machines to work like human behavior. This is a great process that enables companies and organizations to reduce the workload, create qualitative features and expand the business. This intelligence is mainly related to the collection of human behavior, animals, and other living standard to step in their shoe and reconcile that knowledge for better working segments. In fact, this process is easily able to perform normal work that is required by human intelligence; visual effect, speech, decision-making, programming skills, and translation between languages.

What is Artificial Intelligence?

Artificial intelligence is a summary of human intelligence processes that are involved in machines, especially in computers. Artificial intelligence allows users to get several benefits and expansion in using particular software or application for a better learning approach. There are specific applications included under artificial intelligence that promotes better working factors like expert system, natural language processing, speech recognition, dynamic approach, machine vision, and other benefits that create an environment of Human intelligence. Artificial intelligence has a purpose to use for various aspects like;

• that help machines to learn from experience
• Make adjustments to new inputs and
• Think like human nature as well as grab human processing factors.

What are types of Artificial Intelligence!

According to the current system of classification, there are primarily four types of Artificial intelligence are included that create and have the perception to work in different fields of human nature. Basically, this four artificial intelligence aren’t created as equal and have the same perception to work on it but gm function on different aspects they are designed for.

• Reactive Artificial Intelligence is a basic type of artificial intelligence that provides a predictable output on the basis of the input it receives. Basically, it provides the result in the same way that it receives. Example of Reactive AI chess gameplay,
• IBM supercomputers and deep blue.
  Limited Memory Artificial Intelligence, is a widely used artificial intelligence where it works on basis of past performance and historical data to make predictions and complex tasks. This is one of the effective kinds where users learn several aspects and get accurate results for their businesses.
• Theory of Mind AI, this type engages human behavior and culture to think, perform and make decisions for a better scope of organizational growth. Machines with a theory mind will easily learn and understand the factors of human nature and adjust their emotions according to several learning aspects.
• Self-aware AI is the advanced version of Artificial intelligence in which machines have the capability to perform their own tasks and work on several activities with accurate visions and approaches. With this, machines easily facilitate better results and outputs to promote worthy decision-making.

What You’ll Learn

• The course teaches how to make machines much more comfortable to adapt to human nature and behavior in a way to appropriate results.
• Learn how to differentiate different types of artificial intelligence with a view to facilitating better learning aspects for several activities.
• Learn programming with Java, data structure, data algorithm, and basic Internet laboratory to work on multiple aspects without any barriers.
• Learn how to adapt human rights and resources to program functions on various activities that help in making healthy decision making.
• Students will be learning discreet mathematics, English languages, and Commination gaps that promote many intelligence benefits for various aspects.

Artificial Intelligence, AI, is an area of study that explores how to create computer programs that learn to make decisions, reason about data, and communicate with humans. In the MS in AI degree program, students learn to apply creative thinking, algorithmic design, and coding skills to build modern AI systems. Students will gain deep technical training and expertise in our focus areas of machine learning, computer vision, and natural language processing.

The program prepares students to work as Artificial Intelligence Engineers in information technology companies or pursue a Ph.D. degree in computer science.

The 8-course MS in AI program is geared toward students with a computer science undergraduate degree, but we also welcome those with equivalent training and experience, as well as students with gaps in their computing background but strong academic records overall.

Program Description

Artificial Intelligence, AI, is an area of study that explores how to create computer programs that learn to make decisions, reason about data, and communicate with humans. In the MS in AI degree program, students learn to apply creative thinking, algorithmic design, and coding skills to build modern AI systems. Students will gain deep technical training and expertise in our focus areas of machine learning, computer vision, and natural language processing.

The program prepares students to work as Artificial Intelligence Engineers in information technology companies or pursue a Ph.D. degree in computer science.

The 8-course MS in AI program is geared toward students with a computer science undergraduate degree, but we also welcome those with equivalent training and experience, as well as students with gaps in their computing background but strong academic records overall.

Requirements
A total of eight graduate courses (totaling 32 credits) must be completed. Students must take the four required core courses: CAS CS 542 Machine Learning
CAS CS 585 Image and Video Computing
CAS CS 505 Introduction to Natural Language Processing
The remaining four 4-credit courses may be elected from the following lists of courses. At least one course from list A must be chosen:

List A:
CAS CS 504: Data Mechanics
CAS CS 506: Computational Tools for Data Science
CAS CS 562: Advanced Database Applications
CAS CS 565: Algorithmic Data Mining
CAS CS 660: Graduate Introduction to Database Systems
List B:
List B includes all CAS 500+ and GRS courses in computer science. As of fall 2018, these courses are:

CAS CS 507: Introduction to Optimization in Computing and Machine Learning
CAS CS 511: Formal Methods 1
CAS CS 512: Formal Methods 2
CAS CS 520: Programming Languages
CAS CS 525: Compiler Design Theory
CAS CS 530: Analysis of Algorithms
CAS CS 531: Advanced Optimization Algorithms
CAS CS 532: Computational Geometry
CAS CS 533: Spectral Methods for Machine Learning and Network Analysis
CAS CS 535: Complexity Theory
CAS CS 537: Randomness in Computing
CAS CS 538: Fundamentals of Cryptography
CAS CS 548: Advanced Cryptography
CAS CS 552: Introduction to Operating Systems
CAS CS 558: Computer Networks Security
CAS CS 568: Applied Cryptography
CAS CS 581: Computational Fabrication
CAS CS 591: Topics in Computer Science (any section)
CAS CS 591 K1: Topics in Computer Science: Deep Learning
GRS CS 651: Distributed Systems
GRS CS 655: Graduate Computer Networks
GRS CS 680: Graduate Introduction to Computer Graphics
GRS CS 940 and 941: Directed Study: Artificial Intelligence
List C:
List C includes AI-related courses taught in the graduate programs of Mathematics & Statistics and the Electrical & Computer Engineering Departments:

GRS MA 679 Applied Statistical Machine Learning
GRS MA 751 Statistical Machine Learning
GRS MA 681 Accelerated Introduction to Statistical Methods for Quantitative Research
ENG EC 503 Introduction to Learning from Data
ENG EC 520 Digital Image Processing and Communication
ENG EC 719 Statistical Pattern Recognition
ENG EC 720 Digital Video Processing
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Sample Program
3-Semester Sample Program

1st Semester (Fall):

CS 640 Artificial Intelligence
CS 660: Graduate Introduction to Database Systems
CAS CS 507: Introduction to Optimization in Computing and Machine Learning
2nd Semester (Spring):

CS 585: Image and Video Computing
CS 505: Natural Language Processing
CS 542: Machine Learning
3rd Semester (Fall):

CS 591 K1: Deep Learning
CS 940: Directed Study in AI
2-Semester Sample Program

1st Semester (Fall):

CS 680: Computer Graphics
CS 565: Algorithmic Data Mining
2nd Semester (Spring):

CS 585: Image and Video Computing
CS 505: Natural Language Processing
CS 591 K1: Deep Learning
CS 940: Directed Study in AI