Course Details
Subject {L-T-P / C} : BM4612 : Artificial Intelligence and Machine Learning { 3-0-0 / 3}
Subject Nature : Theory
Coordinator : Dr. Mirza Khalid Baig
Syllabus
Background and overview
Overview of terminology, formulations and concepts,
Introduction of main tasks, error and performance metrics, data preparation/annotation,
Components of learning, data representation, linear classification, formulation of ML problem,
Learnability
Hoeffding's inequality, overfitting, performance/complexity, bias/variance trade-off,
End-to-End Machine Learning Project
Feature selection, Feature transformation, model selection and validation, regularisation
Regression
Linear Regression, Polynomial Regression, Logistic Regression, Regularized Linear Models, Logistic Regression
SVM and kernels
Hyperplane, separation with hard margin, soft margin, support vector classification, kernel methods, support vector regression
Unsupervised learning
Clustering, k-means algorithm, PCA
Neural Networks
Logistic regression, gradient descent, Perceptron, MLP, backpropagation,
Course Objectives
- To understand basic concepts, algorithms and techniques of machine learning.
- To develop skills for analysing data and using it for training and deploying machine learning systems.
- To gain understanding of software tools used for implementation of machine learning to solve challenges in healthcare sector.
Course Outcomes
At the end of the course, the student will be able to: <br />1. Gain foundational understanding of machine learning concepts. <br />2. Understand the different statistical and probabilistic concepts which enable machines to learn. <br />3. Develop data pre-processing pipelines for biological and healthcare data. <br />4. Develop machine learning models for classification and regression tasks required in healthcare applications. <br />5. Optimise machine learning models for better performance and easier translation for use in healthcare applications.
Essential Reading
- Hastie, Tibshirani, Freidman, The Element of Statistical Learning, Springer
- Shai Shavlev and Shai Ben-David, Understanding Machine Learning, Cambridge University Press
Supplementary Reading
- Aurelien Geron, Hands-On Machine Learning with Sci-kit Learn, Keras and Tensorflow, O'Reilly
- Ian Goodfellow, Yoshua Bengio, Aaron Courville, Deep Learning, The MIT Press, Cambridge Mass.