CPSC 340 - Machine Learning and Data Mining (Fall 2015)

Tutorials: Mondays from 11-12 (DMP 201), 2-3 (DMP 201), 4-5 (DMP 201), and 5-6 (DMP 101).

Office hours: Tuesdays from 10-11 (ICICS X836, except November 17 when it will be ICICS 146) and 4-5 (ICICS 146), Thursdays from 3-4 (ICICS X836).

Instructor: Mark Schmidt

Teaching Assistants: Ricky (Tian Qi) Chen, Issam Laradji, Bobak Shahriari, Sharan Vaswani, Yan Zhao.

Synopsis: We introduce basic principles and techniques in the fields of data mining and machine learning. These are some of the key tools behind the emerging field of data science and the popularity of the `big data' buzzword. These techniques are now running behind the scenes to discover patterns and make predictions in various applications in our daily lives. We'll focus on many of the core data mining and machine learning technlogies, with motivating applications from a variety of disciplines.

Textbook: There is no required textbook for the class. A introductory book that covers many (but not all) the topics we will discuss is the Artificial Intelligence book of Rusell and Norvig (AI:AMA) or the Artificial Intelligence book of Poole and Mackworth (you may need these for other classes). More advanced books include The Elements of Statistical Learning (ESL) by Hastie et al., Murphy's Machine Learning: A Probabilistic Perspective (ML:APP) which can be accessed through the library here, and Bishop's Pattern Recognition and Machine Learning (PRML). For books with a bigger focus on data mining, see Introduction to Data Mining (IDM) and Mining of Massive DataSets.

Prerequisites:

• Basic algorithms and data structures (CPSC 221, or both of CPSC 260 and 320 as well as one of CPSC 210, EECE 210, EECE 309).
• Linear algebra (one of MATH 152, 221, or 223).
• Probability (one of STAT 200, STAT 203, STAT 251, STAT 302, MATH 302, MATH 318, BIOL 300).

Registration: Undergraduate and graduate students from any department are welcome to take the class, provided that they satisfy the prerequisites. If you do not satisfy the exact prerequisites but would still like to enroll in the class, please fill out the the form available here. If you are interested in these topics but the course is full, please sign up for the waiting list; a certain number of students are likely to shift their schedule which will open up spots, while a long waiting list makes it more likely that we can have multiple sections and multiple courses on these topics. You may also want to consider taking related courses from statistics: STAT 305, STAT 306, STAT 406, STAT 460, STAT 461.

Grading: Assignments 25%, Midterm 30%, Final 45%.

Piazza for course-related questions.

List of topics

• Data exploration, cleaning, and preprocessing.
• Frequency-based supervised learning.
• Data clustering and association rules.
• Linear prediction, regularization, and kernels.
• Latent-factor models.
• Neural networks and deep learning.
• Link analysis and collaborative filtering.
• Sequences and time-series.

Timetable

Related courses that have online notes

• Machine Learning (UBC 2012)
• Machine Learning (Stanford)
• Introduction to Machine Learning (Alberta - Schuurmans)
• Introduction to Machine Learning (Alberta - Greiner)
• Introduction to Machine Learning (Berkeley)
• Machine Learning (MIT)
• Machine Learning (CMU)
• Course in Machine Learning (Maryland)
• Principals of Knowledge Discovery in Data (Alberta)
• Mining Massive Data Sets (Stanford)
• Data Mining (CMU)