Artificial Intelligence and Machine Learning: AI is defined as the development of computer systems capable of performing tasks that typically require human intelligence. In other words, AI enables computers to think and behave more like people to solve problems. Whereas machine learning is a method of analyzing data that helps computer programs optimize their functionality as they learn from vast quantities of data. Machine learning is a specific form of AI that enables computers to learn and grow as they’re introduced to data-based scenarios.
If you’re looking for a solid, secure future, a career as a machine learning engineer is the right one. In fact, it was just listed as the second-most in-demand AI profession, with the pandemic bringing a greater focus on the fields of artificial intelligence and machine learning. Over the past four years, employment in AI and machine learning has increased by approximately 75%, and this growth is expected to continue. Pursuing a machine learning job is a solid choice for a high-paying career that will be in demand for decades. Industries that are already using AI and machine learning predominantly include healthcare, education, marketing, retail and ecommerce, and financial services. A career in machine learning is for you if you want to work on projects that change the world while earning a high salary and benefits.
Vision:
To produce high potential Artificial Intelligence and Machine Learning Professionals with Global Standards who can handle the challenges of the society and industry with their innovations and services.
Mission:
- To impart high quality education with effective teaching and learning process.
- To provide a dynamic, innovative, and flexible curriculum which equips the students with AI problem-driven skills to strengthen their career, pursue higher studies and to enable them to be industry ready.
- To foster inquisitive-driven research among students and staff to reinforce domain knowledge in addressing contemporary societal issues.
- To inculcate professional ethics and human values in handling the engineering challenges.
- To shape the department into a center of academic and research excellence
| S.NO. | NAME OF THE STAFF | DESIGNATION/POSITION | QUALIFICATION |
| 1 | DR. KUMARI.M | PROFESSOR | M.Tech,Ph.D |
| 2 | MOHAMMAD FERNAAZ | ASSISTANT PROFESSOR | M.Tech |
| 3 | JETTY SIVAPARVATHI | ASSISTANT PROFESSOR | M.Tech |
| 4 | M. KISHORE BABU | ASSISTANT PROFESSOR | M.Tech |
Why Study AI/ML at UCET?
The interesting, difficult, and expanding area of artificial intelligence and machine learning has a huge influence on daily life and society worldwide. The Department of AIML at UCET will examine both the theory and application of AI and ML, and classes are instructed by knowledgeable academics who are also engaged in ongoing research. Project-based learning, active learning, and significant hands-on experience will all be given more importance. At UCET, you may use a variety of facilities for learning and doing research.
The most popular topic and probably the most desirable field in both industry and academia right now is artificial intelligence and machine learning. The majority of IT businesses, including Microsoft, Google, Amazon, Tesla, and NVIDIA, have incorporated this idea into their operating systems. An area of computer science that is rapidly expanding focuses on developing and offering intelligent problem-solving solutions is artificial intelligence software, which is produced by the top artificial intelligence businesses. Speech recognition, problem-solving, learning, and planning capabilities of AI software can support or even take the place of human involvement in a process.
In general, the following are the various reasons why every student should learn AIML:
- Bright Career Opportunities and Growth, Average Salary Hike,Reputed Core Hiring Companies: Amazon, Microsoft, Google, Nokia to name a few core companies.
- Artificial Intelligence is Versatile,The Next Digital Frontier,The skill of the Century.
- Processes Huge Amounts of Data Potential Impact on the Society.
- Better User Experience,Deals with Real-world Applications such as Recommender Systems, Prediction, Recognition, Autonomous Vehicles, Robotics, so forth.
Career path you can choose after the course
- Machine Learning Engineer ,Data Scientist,Artificial Intelligence Engineer,Data Analyst,Machine Learning Architect
Types of companies with career opportunities
- Amazon, Apple, Google, Facebook, DJI, Anki, Clarifai, Deepmind
- 1. Engineering knowledge: Apply the knowledge of mathematics, science, engineering fundamentals, and an engineering specialization to the solution of complex engineering problems.
- 2. Problem analysis: Identify, formulate, review research literature, and analyze complex engineering problems reaching substantiated conclusions using first principles of mathematics, natural sciences, and engineering sciences.
- 3. Design/development of solutions: Design solutions for complex engineering problems and design system components or processes that meet the specified needs with appropriate consideration for the public health and safety, and the cultural, societal, and environmental considerations.
- 4. Conduct investigations of complex problems: Use research-based knowledge and research methods including design of experiments, analysis and interpretation of data, and synthesis of the information to provide valid conclusions.
- 5. Modern tool usage: Create, select, and apply appropriate techniques, resources, and modern engineering and IT tools including prediction and modeling to complex engineering activities with an understanding of the limitations.
- 6. The engineer and society: Apply reasoning informed by the contextual knowledge to assess societal, health, safety, legal and cultural issues and the consequent responsibilities relevant to the professional engineering practice.
- 7. Environment and sustainability: Understand the impact of the professional engineering solutions in societal and environmental contexts, and demonstrate the knowledge of, and need for sustainable development.
- 8. Ethics: Apply ethical principles and commit to professional ethics and responsibilities and norms of the engineering practice.
- 9. Individual and team work: Function effectively as an individual, and as a member or leader in diverse teams, and in multidisciplinary settings.
- 10. Communication: Communicate effectively on complex engineering activities with the engineering community and with society at large, such as, being able to comprehend and write effective reports and design documentation, make effective presentations, and give and receive clear instructions.
- 11. Project management and finance: Demonstrate knowledge and understanding of the engineering and management principles and apply these to one’s own work, as a member and leader in a team, to manage projects and in multidisciplinary environments.
- 12. Life-long learning: Recognize the need for, and have the preparation and ability to engage in independent and life-long learning in the broadest context of technological change.