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ML and AI techniques are used for pattern recognition and classification problems. ML and AI allow computers to answer complex and hard-to-solve problems via automation. This need has come into prominence due to the emergence of big data where it's necessary to automate methods to identify patterns to predict future data and train machines.

Machine Learning & AI @É«É«Ñо¿Ëù

Machine Learning & AI at É«É«Ñо¿Ëù is overseen by recognized experts in the the field whom perform ongoing scientific research with industry support. There are many exciting research opportunities for our students to participate in with our Bioinformatics and Machine Learning (BML) lab, the Canizaro Livingston Gulf States Center for Environmental Informatics. Ongoing research at É«É«Ñо¿Ëù includes applications in machine learning and artificial intelligence.

Opportunities

In Louisiana, the demand for experts in ML and AI is high. A large number of companies and federal agencies are seeking experts in ML and AI including: IBM Baton Rouge, GDIT,Radiance Technologies, Choices, Lucid, AWS, Sirius Computer Technologies, Danaher, Cynet Systems, Device Medical Products, Ochsner Health System, Acuity One LLC, Salient CRGT, U.S. Navy, Bennett Aerospace, Entergy and the U.S. Army Corps of Engineers - New Orleans District.

Objectives of Machine Learning & AI Concentration

• Impart an understanding of the fundamental issues and challenges of Machine Learning and Artificial Intelligence, including large data collections, model selection, model complexity, standard algorithms and techniques.
• Acquire a conceptual understanding of the strengths and weaknesses of Machine Learning approaches that are commonly used in industry and elsewhere, together with their theoretical underpinnings
• Explore the underlying mathematical relationships within and across Machine Learning and Artificial Intelligence algorithms, including the paradigms of supervised and un-supervised learning.
• Design and implement various Machine Learning and Artificial Intelligence algorithms in a range of real-world applications.

Highlights of Machine Learning & AI Concentration

Python Programming Language: The most popular programming language for artificial intelligence and machine learning applications

Artificial Intelligence: Topics include knowledge representation, search strategies, and surveys of principal subareas of artificial intelligence such as expert systems, natural language processing, reasoning systems, games, learning, and vision.

Natural Language Processing (NLP): A branch of artificial intelligence that deals with the interaction between computers and humans using the natural language. The ultimate objective of NLP is to read, decipher, understand, and make sense of the human languages in a manner that is valuable

Machine Learning (Differential approaches): A probabilistic perspective of machine learning as well as the algorithms, in the real world, such as Dynamic Programming, Exhaustive Search, Combinatorial Pattern Matching, Clustering and Trees, Hidden Markov Models, Greedy and Randomized Algorithms, Graph Algorithms. Emphasizing the programming aspects of these topics.

Machine Learning (Non-Differential approaches): Topics include Machine Learning Models: Neural Networks, Support Vector Machines, Boosting, Genetic Algorithms, Decision Trees, Random Forests, and Deep Belief Nets. Emphasizing the programming aspects of these topics.