Fundamentals of Machine Learning

UNIT I

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

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

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

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.

Text Books:

1. Tom M. Mitchell, Machine Learning, McGraw-Hill, 2010.
2. Ethem Alpaydin, Introduction to Machine Learning, MIT Press, Pearson, Third Edition 2014.
3. Bishop, Christopher. Neural Networks for Pattern Recognition. New York, NY: Oxford University Press, 1995

Reference Books:

1. Ethem Alpaydin, (2004), Introduction to Machine Learning (Adaptive Computation and Machine Learning), The MIT Press
2. T. Astie, R. Tibshirani, J. H. Friedman, The Elements of Statistical Learning, Springer (2nd ed.), 2009
3. Bishop, C., Pattern Recognition and Machine Learning. Berlin: Springer-Verlag