This is an introductory course on Machine Learning (ML) that is offered to undergraduate and graduate students. The contents are designed to cover
both theoretical and practical aspects of several well-established ML techniques. The assignments will contain theory and programming questions that
help strengthen the theoretical foundations as well as learn how to engineer ML solutions to work on simulated and publicly available real datasets.
The project(s) will require students to develop a complete Machine Learning solution requiring preprocessing, design of the classifier/regressor, training
and validation, testing and evaluation with quantitative performance comparisons.
- Explain the different types of learning problems along with some techniques to solve them.
- Model real-world problems, apply different learning techniques and quantitatively evaluate the performance.
- Identify and use advanced techniques through existing machine learning tools and libraries.
- Analyze performance of ML techniques and comment on their limitations.
- Introduction: Introduction to Machine Learning; Deep Learning vs Machine Learning; Review
- Empirical Risk Minimization: Learning Problems and the Risk Minimization Framework
- Regression: Linear and Logistic Regression
- Machine Learning in Practice: Bias-Variance; Training/Testing; Overfitting; Jackknifing/Cross-Validation; Occam’s razor; Regularization and Model Selection
- Classification: Naïve Bayes; Decision Trees; Random Forests
- Neural Networks: Perceptron; Backpropagation; Training Neural Networks
- Support Vector Machines: Optimization basics; Primal Form; Dual Form; Kernel SVMs, Soft-Margin SVMs
- Unsupervised Learning: k-means clustering; Spectral Clustering (if time permits)
- Deep Learning: Convolutional Neural Networks and applications to Images and Speech
- Ethical Issues: Fairness, Accountability, and Transparency in Machine Learning
- Machine Learning, Tom M. Mitchell, McGraw Hill, 1997
- Pattern Classification, Duda, Hart and Stork. 2nd ed., Wiley, 2006
- Pattern Recognition and Machine Learning, Christopher M. Bishop, Springer, 2006
- Understanding Machine Learning: From Theory to Algorithms, Shai Shalev-Shwartz and Shai Ben-David, Cambridge University Press, 2014
- Neural Networks and Learning Machines, 3rd Edition, 2009
Related Journals and Conferences [!Exhaustive]
- Journal of Machine Learning Research
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- IEEE Transactions on Neural Networks and Learning Systems
- Neural Information Processing Systems
- International Conference on Machine Learning
- Association for the Advancement of Artificial Intelligences
- Computer Vision and Pattern Recognition
- International Conference on Computer Vision
- Google Classroom will be used as primary mode of course administration, including general announcements, announcements/submission of assignments, doubt resolution.
- Colab will be used for Coding Assignments.
- Course will be supported with teaching assistants (TAs). TAs will communicate their office hours individually.
- TAs will support the course through doubt resolution, responsible for announcement and/submission of quizzes/assignments, grading with faculties supervision etc.
- The course follows a zero tolerance on academic dishonesty.
The course follows the IIIT-Delhi policy on Academic Dishonesty
[Updated Institute policy will apply]
- All plagiarism cases will be on record for your tenure at IIIT-Delhi
- All code and reports will be checked for plagiarism
- Home work theory questions may be asked in exam or quiz as it is
- If correct in Home work and incorrect in exam, home work question will be marked zero. Make sure you know all your home work theory questions.
- Students need to submit all assignments and projects on time.
- Students should achieve a grade of at least the class average.
- Audit students are not permitted to do projects with registered students.
- Quizzes and Exams are optional.