IP University
B-tech AI-ML
Semester 4 FML Notes
Foundations of Machine Learning
AI-ML Unit 1
Full Unit 1
Machine Learning Basics
Artificial Intelligence
FML Lecture Notes
IP University B-tech
AI-ML Course
Machine Learning Algorithms
AI-ML Exam Preparation
FML Study Material
Machine Learning Tutorial
AI-ML Semester 4
Machine Learning Fundamentals
IP University AI-ML Course
AI-ML FML Syllabus
Introduction to Machine Learning
Reinforcement Learning
Introduction to Reinforcement Learning
Methods of Reinforcement Learning
Elements of Reinforcement Learning
Bellman Equation
Markov Decision Process (MDP)
Q Learning
Value Function Approximation
Temporal Difference Learning
Neural Networks in Reinforcement Learning
Deep Q Neural Network (DQN)
Applications of Reinforcement Learning
Machine Learning
Artificial Intelligence
AI Algorithms
Deep Learning
Self-learning Algorithms
Reinforcement Learning Tutorial
RL Basics
RL Concepts
RL Explained
RL Algorithms
RL Implementation
RL in Gaming
RL in Robotics
AI Applications
Technology and AI
Online Learning
Educational Content
Online Courses
Learning Resources
Programming
Computer Science
Data Science
AI Research
AI Development

Course Overview:

This course covers fundamental concepts and methods of computational data analysis, including pattern classification, prediction, visualization, and recent topics in machine learning. The course will give the student the basic ideas and intuition behind modern machine learning methods as well as a bit more formal understanding of how, why, and when they work. The underlying theme in the course is a statistical inference as it provides the foundation for most of the methods covered.

UNIT I:

[10]

Introduction to machine learning- Basic concepts, developing a learning system, Learning Issues, and challenges. Types of machine learning: Learning associations, supervised, unsupervised, semi- supervised and reinforcement learning, Feature selection Mechanisms, Imbalanced data, Outlier detection, Applications of machine learning like medical diagnostics, fraud detection, email spam detection

UNIT II:

[10]

Supervised Learning- Linear Regression, Multiple Regression, Logistic Regression, Classification; classifier models, K Nearest Neighbour (KNN), Naive Bayes, Decision Trees, Support Vector Machine (SVM), Random Forest

UNIT III:

[10]

Unsupervised Learning- Dimensionality reduction; Clustering; K-Means clustering; C-means clustering; Fuzzy C means clustering, EM Algorithm, Association Analysis- Association Rules in Large Databases, Apriori algorithm, Markov models: Hidden Markov models (HMMs).

UNIT IV:

[10]

Reinforcement learning- Introduction to reinforcement learning, Methods and elements of reinforcement learning, Bellman equation, Markov decision process (MDP), Q learning, Value function approximation, Temporal difference learning, Concept of neural networks, Deep Q Neural Network (DQN), Applications of Reinforcement learning.