CS 360: Introduction to Data Science &
Machine Learning
— Spring 2019
This is a course desinged for senior undergraduate students. This course will cover introductory materials on Data Science and Machine Learning. The course schedule will list the set of topics will be covered in this course.
Instructor
Clint P. George — clint [at] iitgoa.ac.in — Office: 205 (F5), IT Building
Meetings
Lectures: Monday (2-3pm, CL3) — Tuesday (3-4pm, LH2) — Wednesday (11:30am-12:30pm, T1)
Office hours: Monday (3-4pm) — Tuesday (4:00pm-4:55pm)
Recommended Books
- Elements of Statistical Learning
- Machine Learning
- Pattern Recognition and Machine Learning (Information Science and Statistics)
- Foundations of Data Science
- R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
Course Eligibility and Requirements
- This is a core course designed for the fifth semester Computer Science and Engineering undergraduate students. Knowledge in computer programming is required.
- Course prerequisites: CS 113, MA106, CS215, CS218, MA214, CS344, CS386
Grading Policy
Quiz 1 (10%) — Midterm (20%) — Quiz 2 (10%) — Seminar (10%) — Homework and Classroom Pop-Quizes (10-15%) — Final (35-40%)
Academic Honesty
We expect each student to follow the highest standards of integrity and academic honesty. Copying/sharing code in exams, homeworks, labs are not allowed: see IIT Goa: Policy for academic malpractices.
Course Schedule
This is a tentative course schedule. It will be updated often. Log on to classroom to see lecture slides, other course materials, and announcements.
| # | Topic | Materials* | |
|---|---|---|---|
| 1 | Course Introduction | ||
| 2 | R programming, data visualization, data transformations using R libraries | ||
| 3 | Exploratory data analysis | ||
| 4 | Expected value, variance, The Central Limit Theorem | ||
| 7 | Linear regression, logistic regression, Perceptron (review) | ||
| 8 | Generative learning algorithms, Gaussian discriminant analysis | ||
| 9 | MLE, MAP (review) | ||
| 10 | Support Vector Machines (SVMs), kernel methods | ||
| 11 | Bias--variance tradeoff and error analysis | ||
| 12 | Learning Theory, Generalization errors, VC dimension | ||
| 13 | Regularization and model selection | ||
| 14 | Experimental evaluation of learning algorithms, cross-validation | ||
| 15 | Multilayer neural networks | ||
| 16 | Backpropagation | ||
| 18 | Mixture models and mixture of Gaussians | ||
| 19 | The expectation maximization (EM) algorithm | ||
| 22 | Probabilistic topic models | ||