Course Description
Machine Learning is the discipline of designing algorithms that allow machines (e.g., a computer) to learn patterns and concepts from data without being explicitly programmed. This course will be an introduction to the design (and some analysis) of Machine Learning algorithms, with a modern outlook, focusing on the recent advances, and examples of real-world applications of Machine Learning algorithms. This is supposed to be the first ("intro") course in Machine Learning. No prior exposure to Machine Learning will be assumed. At the same time, please be aware that this is NOT a course about toolkits/software/APIs used in applications of Machine Learning, but rather on the principles and foundations of Machine Learning algorithms, delving deeper to understand what goes on "under the hood", and how Machine Learning problems are formulated and solved.
Grading
There will tentatively be 4-5 homeworks (may include a programming component) worth 50% of the total grade, and another evaluation component will be via online quizzes/exams worth 50% of the total grade. We do plan to have online mid-sem and online end-sem exams but, given the current uncertainty regarding logistics of their conduct, we are unable to specify the exact break up of various online quizzes/exams (but, overall, they will be worth 50%).
Schedule
The schedule and the course material will be posted weekly on the mooKIT website for this course.
Reference materials
There will not be any dedicated textbook for this course. We will use lecture slides/notes, monographs, tutorials, and papers for the topics that will be covered in this course. Some recommended, although not required, reference books are listed below (in no particular order):
- Hal Daumé III, A Course in Machine Learning (CIML), 2017 (freely available online)
- Kevin Murphy, Machine Learning: A Probabilistic Perspective (MLAPP), MIT Press, 2012
- Christopher Bishop, Pattern Recognition and Machine Learning (PRML), Springer, 2007.
- David G. Stork, Peter E. Hart, and Richard O. Duda. Pattern Classification (PC), Wiley-Blackwell, 2000
- Ian Goodfellow and Yoshua Bengio and Aaron Courville. Deep Learning (DL), MIT Pess, 2016 (individual chapters freely available online)
- Trevor Hastie, Robert Tibshirani, Jerome Friedman, The Elements of Statistical Learning (ESL), Springer, 2009 (freely available online)
- Shai Shalev-Shwartz and Shai Ben-David. Understanding Machine Learning: From Theory to Algorithms (UML), Cambridge University Press, 2014
- Mehryar Mohri, Afshin Rostamizadeh and Ameet Talwalkar. Foundations of Machine Learning (FOML), MIT Press, 2012
Other useful references
Here is a book on essential Maths for Machine Learning (here is the PDF copy)
Here is another useful, interactive (Python notebooks) book on deep learning (it also covers many of the basic topics in machine learning): Dive into Deep Learning (authors: Aston Zhang, Zack C. Lipton, Mu Li, Alex J. Smola)