## Machine Learning (CS60050)## Spring semester 2018-19## Announcements**End-semester syllabus includes all topics taught in the course.**- Assignment 4 declared - deadline April 19 - see below.
- Assignment 3 declared - deadline March 31 - see below.
- Assignment 2 declared - deadline March 15 - see below.
- Assignment 1 declared - deadline Feb 15 - see below.
- Every student should create an account on Moodle submission system of CSE department. This system will be used for submission and grading of assignments. Go to this link and follow the link "Moodle" (bottom-left on page). Create a new account for yourself (unless you have an account already), giving username, password, email id. After creating an account, login to the system, and follow the link "Spring Semester (2018-19)". Choose the course "Machine Learning". Join this course as "Student"; use Student Enrolment Key: STUML.
- All registered students should join the mailing group https://groups.google.com/d/forum/machinelearning2019
## InstructorSaptarshi Ghosh (Contact: saptarshi @ cse . iitkgp . ac . in)
## Teaching Assistants- Abhisek Dash (assignmentad @ gmail . com)
- Paheli Bhattacharya (pahelibhattacharya @ gmail . com)
- Shalmoli Ghosh (shalmolighosh94 @ gmail . com)
- Ainuddin Khan (ainuddin.india @ gmail . com)
- Harish Yadav (harishyadav394 @ gmail . com)
- Midatala Surya (surya.midatala @ gmail . com)
## Course Timings (3 lectures)Wednesday 11:00 - 11:55Thursday 12:00 - 12:55 Friday 08:00 - 08:55 ## Class venue: NR421 (Nalanda complex)## Course evaluationAssignments: 40% (There will be 4-5 assignments that will involve programming in C/C++/Java/Python) Mid-semester exam: 20% End-semester exam: 40%## Topics (outline)**Introduction**: Basic principles, Applications, Challenges**Supervised learning**: Linear Regression (with one variable and multiple variables), Gradient Descent, Classification -- Logistic Regression, Decision Trees, Naive Bayes, Support Vector Machines, Artificial Neural Networks (Perceptrons, Multilayer networks, back-propagation)**Unsupervised learning**: Clustering (K-means, Hierarchical), Dimensionality reduction**Ensemble learning**: Bagging, boosting**Theory of Generalization**: In-sample and out-of-sample error, Bias and Variance analysis, Overfitting, Regularization, VC inequality, VC analysis,**Advanced topics**: Bias and fairness in Machine Learning
## Text and Reference Literature- Christopher M. Bishop. Pattern Recognition and Machine Learning (Springer)
- David Barber, Bayesian Reasoning and Machine Learning (Cambridge University Press). Online version available here.
- Tom Mitchell. Machine Learning (McGraw Hill)
- Richard O. Duda, Peter E. Hart, David G. Stork. Pattern Classification (John Wiley & Sons)
## AssignmentsAssignment 1 (Linear regression):Question Deadline: February 15, 2019, 23:59 pm IST Evaluation by TAs: Sl. no. 01-31: Shalmoli | 32-62: Ainuddin | 63-92: Harish | 93-123: Surya [Serial numbers according to the list of students given below] Assignment 2 (Decision Trees): Question Deadline: March 15, 2019, 23:59 pm IST Evaluation by TAs: Sl. no. 01-31: Surya | 32-62: Shalmoli | 63-92: Ainuddin | 93-123: Harish [Serial numbers according to the list of students given below] Assignment 3 (Clustering): Question Deadline: March 31, 2019, 23:59 pm IST Evaluation by TAs: Sl. no. 01-31: Harish | 32-62: Surya | 63-92: Shalmoli | 93-123: Ainuddin [Serial numbers according to the list of students given below] Assignment 4 (Neural Networks): Question Deadline: April 19, 2019, 23:59 pm IST Evaluation by TAs: Sl. no. 01-31: Ainudding | 32-62: Harish | 63-92: Surya | 93-123: Shalmoli [Serial numbers according to the list of students given below] List of 123 students in the course: pdf ## Slides
## Other interesting stuff |