Machine Learning Course Outcomes : Upon completion of the subject, students will be able to 1. Apply the concepts of concept learning to solve well posed problems. L3 2. Apply decision trees learning, artificial neural networks and evaluation hypotheses for the machine learning problems. L3 3. Compare and contrast Bayesian, Computational and Instance-based learning techniques. L3 4. Choose correct learning set of rules for machine learning problems using analytical learning. L3 5. Apply inductive and analytical approaches to learn reinforcement learning and Q learning. L3 ___________________________________________________________________________________ For More Understanding, Students can refer to the below material. This is for student(s) reference only. 1. Text Book TOM M. MITCHELL -- Download 2. Lesson Plan ----- Download 3. Unit Wise Question Bank- Download 4. Tennis Dataset 5. Salary Dataset ...
DEEP LEARNING - R25 Deep learning is a method in artificial intelligence (AI) that teaches computers to process data in a way that is inspired by the human brain . Deep learning models can recognize complex patterns in pictures, text, sounds, and other data to produce accurate insights and predictions. Course Objectives: 1. To understand the complexity of Deep Learning algorithms and their limitations 2. To be capable of performing experiments in Deep Learning using real-world data. Course Outcomes: 1. Implement deep learning algorithms, understand neural networks and traverse the layers of data 2. Learn topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces 3. Understand applications of Deep Learning to Computer Vision 4. Understand and analyze Applications of Deep Learning to NLP SYLLABUS COPY 1. THEORY SYLLABUS- CLICK HERE 2. LAB SYLLABUS- CLICK HERE __________________________...