Syllabus (COMPUTER SCIENCE)

Course Type: MAJ-14

Semester: 8

Course Code: BCOSMAJ14T

Course Title: Machine Learning

(L-P-Tu): 4-0-0

Credit: 4

Practical/Theory: Theory

Course Objective: • To learn the basic concepts and techniques of Machine Learning. • To develop the skills required for machine learning technologies to analyze data and create visualizations.

Learning Outcome: • To understand the concepts of machine learning and types of problems tackled by machine learning • To explore the different supervised learning techniques including ensemble methods • To learn different aspects of unsupervised learning and reinforcement learning • To learn the role of probabilistic methods for machine learning • To understand the basic concepts of deep neural network model and design the same

Theory

Introduction

Basic definitions, Types of Learning, Designing a learning System, Inductive Bias and Hypothesis, Hypothesis Evaluation.(5 Lectures)

Supervised Learning

Supervised learning basics, Artificial Neural Network, Training and Testing, Classification and Regression, classifying with k-Nearest Neighbors, splitting datasets one feature at a time: decision trees, classifying with probability theory: naive Bayes, Logistic regression, Support vector machines, Improving classification with the AdaBoost meta-algorithm. (15 Lectures)

Unsupervised Learning

Grouping Unlabeled Items using K-Means Clustering, Agglomerative and Divisive Clustering, Association Analysis with The Apriori Algorithm, Efficiently Finding Frequent Itemset With FP-Growth. (15 Lectures)

Reinforcement learning

Markov decision process (MDP), Bellman equations, Value iteration and policy iteration, Linear quadratic regulation (LQR), Linear Quadratic Gaussian (LQG). (15 Lectures)

Additional Tools

Dimensionality reduction: Feature Extraction - Principal component analysis to simplify data, Simplifying data with the singular value decomposition, Feature Selection. (10 Lectures)

Reading References

1. Introduction to Machine Learning by Ethem Alpayd in, PHI Learning.

2. Machine Learning: An Algorithmic Perspective by Stephen Marsland, Chapman and Hall/CRC.

3. Pattern Recognition and Machine Learning by Christopher M. Bishop, Springer.

4. Machine Learning by Tom Mitchell, McGraw Hill Education