Syllabus of IP university ist and second semester
The Art of Game Design A Book of Lenses
Available below is the book 'The Art of Game Design A Book of Lenses ' by Jesse Schell in a pdf format. Useful for students of Computer Science, studying Artificial Intelligence.Topics included are What Skills Does a Game Designer Need?, The Five Kinds of Listening, Introspection: Powers, Perils, and Practice, etc.
Artificial Intelligence- IP University
Sharing the notes of ARTIFICIAL INTELLIGENCE in this Attachment. The content includes topics such as Cybernetics And Brain Simulation, Symbolic, Cognitive Simulation, Logic-based, "Anti-logic" or "scruffy", Knowledge-based, Sub-symbolic, Bottom-up, Embodied, Situated, Behavior-based Or Nouvelle AI, Computational Intelligence And Soft Computing, etc.
basics of MACHINE LEARNING
The document attatched below is a collection of ML basics and tutorials to help you understand ML better. Topics included are How Machine Learning Works, Supervised Learning, Unsupervised Learning, How Do You Decide Which Algorithm to Use?, When Should You Use Machine Learning?, Real World Examples, etc.
What is Artificial Intelligence?
Of late we have been surrounded by the terms 'artificial intelligence, 'machine learning' and 'data science'. All the leading economies around the globe, have already begun investing in this domain. Extensive research work in this direction has started to become ubiquitous. But the question is, what are these terms exactly, do they bear any relation with each other and why is everyone discussing about them? Machine learning and data analytics, essentially form the backbone of artificial intelligence. Simply put, artificial intelligence is the ability of your machine to think independently, without the intervention of the user and produce results. The machine needs to taught first, only then can it start thinking and produce results. But, the interesting point to ponder over is, how can we teach our machine to think, to produce results. Well, this can be achieved using machine learning and data analytics. The underlying principle is, teach your machine (machine learning) using the past data (data analytics) to make your machine think (artificial intelligence). In machine learning, we make our machine "learn" based on various algorithms and the past data. The machine uses the data of the past, to understand the trend, and employing the data, it tries to make prediction based on various algorithms. Machine learning can be broadly divided into two sub-categories namely: - 1. Supervised learning 2. Unsupervised learning In supervised learning, the data, which we use for training our machine, is split into two parts. The first set, is used for training or "teaching" our model while the second set, is used for evaluating our trained model, by comparing the values generated by our model with the known values (often referred to as a ‘label’). Whereas in the case of unsupervised learning, the whole of the data is used for training the model. Also, an integral part of ML, are the algorithms one employs for training the machine. Machine learning algorithms are broadly classified into the following three categories: - 1. Classification: - This category simply deals with dividing the data into different categories based on a given criteria. A simple example of this could be, given the details of a patient, whether that patient is diabetic or not can be put under this category. 2. Regression: - This algorithm deals with problems where a numerical output is expected. This deals with questions such as “how much” or “how many”. 3. Clustering: - As the name suggests, clustering means putting the data into groups or clusters. The backbone of machine learning is data analytics. Simply put, data analytics pertains to using the past data to draw results in the present, and also predict about the future. Hence, it won’t be wrong to say that the data is of paramount importance to us. Using the data, the machine learns and subsequently uses, whatever it has learnt to come up with a result. P.S -- This is my first article on this platform. Constructive feedback on the same will be highly appreciated.
ARTIFICIAL INTELLIGENCE PPT
Sharing the notes of ARTIFICIAL INTELLIGENCE in this Attachment. The content includes topics such as Cybernetics And Brain Simulation, Symbolic, Cognitive Simulation, Logic-based, "Anti-logic" or "scruffy", Knowledge-based, Sub-symbolic, Bottom-up, Embodied, Situated, Behavior-based Or Nouvelle AI, Computational Intelligence And Soft Computing, etc. Watch related videos on this link - https://www.youtube.com/results?search_query=ARTIFICIAL+INTELLIGENCE+PPT+Ajaze+khan
ARTIFICIAL NEURAL NETWORKS PPT
Notes available for computer science students who have taken artificial intelligence as a subject. The attatchment includes notes on ARTIFICIAL NEURAL NETWORKS in brief. Content included in the attatchment are as follows INTRODUCTION, HISTORY, BIOLOGICAL NEURON MODEL, ARTIFICIAL NEURON MODEL, ARTIFICIAL NEURAL NETWORK, NEURAL NETWORK ARCHITECTURE, LEARNING, BACKPROPAGATION ALGORITHM, APPLICATIONS and ADVANTAGES Watch related videos on this link - https://www.youtube.com/watch?v=jy5jlionbsE
CSE ROBOTICS PPT
Sharing below the notes of CSE ROBOTICS in brief. Available for topics such as The Three Laws Of Robotics, Types of Robots, Mobile Robots, Walking Robots, Autonomous Robots, Remote-control Robots, Components of ROBOTS, etc. Watch related videos on these links - https://www.youtube.com/watch?v=Cs_VDVQylGw https://www.youtube.com/watch?v=BNX2zrMGRZk