Illinois Institute of Technology offers a masters program in Computational Intelligence for those that are interested in learning about the different ways that computers learn and adapt based upon data. This method is studied as a way of determining how computers solve various complex computer science problems. This program is geared toward recent computer science graduates who want to build on their current knowledge, computer science professionals who are already employed in business, government, or industry professions or people without a degree who want to prepare for a career in the CS industry.
The MCS curriculum provides both core courses and student-selected study areas. The combination of these two types of courses provides a conceptual and practical education for the student. The MCS program is flexible, allowing students to choose a masters project or course work only. An MCS with a specialization in Computational Intelligence generally takes two years to earn.
The eligibility for a specialization in Computational Intelligence requires that a student have a satisfied all his or her general Masters of Computer Science requirements before beginning this masters program. A total of four specialization courses are required as well. Students can choose from;
- CS 480 Artificial Intelligence: Planning and Control
- CS 512 Computer Vision
- CS 522 Data Mining
- CS 579 Online Social Network Analysis
- CS 583 Probabilistic Graphical Models
- CS 584 Machine Learning
- CS 585 Natural Language Processing
This course covers computational methods. These methods focus on the control of autonomous agents. This course aims to give students a real world view by focusing on how the materials are used. Programming paradigms, flexible and reactive systems, and their development are also covered. This course is designed to introduce students to AI planning and control.
COmputer vision is a course that introduces students to the basic subject areas associated with this topic. Statistical applications, estimation strategies, feature extraction, and statistical filtering are some of the topics covered. This course will cover a variety of other topics as well. Other covered topics include epi-polar geometry, recognition, and camera calibration. This course will cover all the different subject areas that fall under computer vision like feature extraction, model reconstruction, motion estimation, and probabilistic modeling. These topics will introduce the student to computer vision by using a comprehensive approach.
Data mining explores complex algorithms in detail. This course will also explore new findings and include a project that requires the application of learned data mining skills.
A comprehensive approach to the study of algorithms, the use of technology in public health, politics, and text analysis, from a fundamental standpoint, will be some of the subject matter studied in this course. There will be a focus on structure and content, community findings, diffusion, and graph dynamics. The scope and impact of distance, search, and influence will be discussed as well. Social topics will be inclusive as well, with a focus on crisis response, social networks, social graph theory, and marketing. The student will be able to explore and get a clear understanding of how each concept works.
Probabilistic graphical models, directed and undirected models, factor graphs, representations, and conditional random areas will be discussed in this course. The discussions will also cover the theoretical aspects of representation, learning, and inference. Other applications in computer vision, natural language processing, computational biology, and medical diagnosis will also be a focus area.
This course introduces the basic problems associated with machine learning and places emphasis on understanding, different techniques, mathematical concepts, and algorithms. The limitations of various machine learning algorithms are discussed and evaluated. This course will include; introduction, regression, kernel methods, generative learning, discriminative learning, neural networks, support vector machines, and graphical models. There will also be periods of unsupervised learning and instruction on dimensionality reduction.
This course introduces computing with human languages. It also covers parsing and semantic representations. Additional focus areas will introduce text generation, lexicography, and discourse as well as Sub-language studies. In addition, applications for study include; CAI, database interfaces, and information retrieval.
To attain a Masters of Computer Science with a Specialization in Computational Intelligence you must first complete the general Masters of Computer Science requirements. Once these requirements have been met, four specialized computer science courses must be completed as well. The general MCS program with a specialization in Computational Intelligence requires 30 credit hours and four specialized courses. Students can select the specialization courses they are most interested in. The course schedules allow students to choose both day and evening courses. Admission into the MCS program requires a bachelors degree, a transcript, GRE and TOEFL scores. However, the bachelor’s degree doesn’t have to be in computer science. Students can complete an MCS with a specialization through distance learning as well. This is available through IIT online. On-demand classes are available with no need for a campus visit if preferred by the student.
Attaining a masters degree in Artificial Intelligence requires a background in general computer science as a prerequisite. Once these requirements have been satisfied the student must choose four specialized courses. The core requirements require 30 academic hours of course work in addition to the four specialized classes. In addition, the completion of this degree usually takes two years. Any student who has an interest, curiosity, and desire to learn more about the myriad of ways that computers learn and adjust based on data as a way of solving complex problems in many different areas of computer science. The interest in this type of degree is often developed as a way of furthering a career in the industry, getting a related advanced degree, and supplementing related work in the industry, however, the interest in this degree can be cultivated by many different things. The flexibility and variety that a degree in this field can offer make it possible for a student to pursue a degree in this field in the classroom or online. This adds the convenience and the flexibility to pursue an MCS degree in Computational Intelligence in a way that’s best suited for the individual.