Artificial intelligence is an ever expanding and dynamic field. The programme is aimed at teaching the students ways of building artificial intelligence in a simple language. Ever since the advent of computers, humans have tried to make computer systems capable of handling everyday tasks.This programme will introduce the basic principles of artificial intelligence research. It will cover simple representation schemes, problem-solving paradigms, constraint propagation, and search strategies. Areas of application such as knowledge representation, natural language processing, expert systems, vision, and robotics will be explored. The LISP programming language will also be introduced.
What do self-driving cars, face recognition, web search, industrial robots, missile guidance, and tumor detection have in common? They are all complex real world problems being solved with applications of intelligence (AI). This course will provide a broad understanding of the basic techniques for building intelligent computer systems and an understanding of how AI is applied to problems. You will learn about the history of AI, intelligent agents, state-space problem representations, uninformed and heuristic search, game playing, logical agents, and constraint satisfaction problems. Hands on experience will be gained by building a basic search agent.
The intelligence exhibited by machines or software, and the branch of computer science that develops machines.
A branch of artificial intelligence, concerns the construction and study of systems that can learn from data.
Robotics is a branch of engineering that involves the conception, design, manufacture, and operation of robots.
Students completing this course will have an in-depth understanding of three core areas of AI and the connections among them, and with such other key AI areas as machine learning, robotics, natural language processing, and multi-agent systems.
Matlab makes deep learning easy. In the Matlab environment, it is easy to visualize deep learning models, debug them, take advantage of GPUs, do fast inference, and interoperate with open source deep learning frameworks. Help us continue to enhance the breadth of our deep learning features.
TensorFlow is the second machine learning framework that Google created and used to design, build, and train deep learning models. You can use the TensorFlow library do to numerical computations, which in itself does not seem all too special, but these computations are done with data flow graphs. In these graphs, nodes represent mathematical operations, while the edges represent the data, which usually are multidimensional data arrays or tensors, that are communicated between these edges.
Keras is a high-level neural networks API, written in Python and capable of running on top of either TensorFlow or Theano. It was developed with a focus on enabling fast experimentation. One of the most powerful and easy-to-use Python libraries for developing and evaluating deep learning models is Keras.
In this course, you will learn the basics of deep learning, and build your own deep neural networks using PyTorch. You will get practical experience with PyTorch through coding exercises and projects implementing state-of-the-art AI applications such as style transfer and text generation.