CMSC  422  Introduction to Machine Learning

Class:  T, Th......11:00am-12:15 pm (CSI 2120)

Instructor: Ramani Duraiswami E-mail: ramani AT cs.umd.edu;

Office Hours:  Wednesdays 12:00 noon - 1:15, in AVW 3361. (you must confirm I am there before coming via a private message on Piazza)

TA: Yang Jiao; E-mail: jiaoyang.tju AT gmail.com

Office Hours: 1:00 PM to 3:00 PM on Fridays, in AVW 3368

Textbook:  A Course in Machine Learning (Required): http://ciml.info

PIAZZA for peer-to-peer discussions/assistance.

LAPTOPS DISCOURAGED IN CLASS

Prerequisites: Programming, algorithms, advanced calculus. 

Description in the catalog: Machine Learning studies representations and algorithms that allow machines to improve their performance on a task from experience. This is a broad overview of existing methods for machine learning and an introduction to adaptive systems in general.

Collaboration Policy:  You may study together and discuss problems and methods of solution with each other to improve your understanding. You are welcome to discuss assignments in a general way among yourselves, but you may not use other students' written work or programs. Use of external references for your work should be cited. Clear similarities between your work and others will result in a grade reduction for all parties. Flagrant violations will be referred to appropriate university authorities.

You are responsible for checking this page, Piazza and Canvas.

Policy: Honor code http://www.studenthonorcouncil.umd.edu/code.html

Previous versions of this course: (for reference)  2016 (Prof. Carpuat); 2013 (Prof. Daume' III)

All assignments will be posted on Canvas
All readings will be based on version 0.99 of the CIML book
All discussions will be on Piazza

1	26 Jan	Introduction
2	31 Jan	Supervised Learning Concepts	CIML 1
3	2 Feb	Decision Trees	CIML 1, CIML 2
	7 Feb	Limits of learning; Underfitting/Overfitting	CIML 1, CIML 2
3	9 Feb	Geometry and nearest neighbors	CIML 3-3.3
	14 Feb	K-means clustering	CIML 3.4-3.5
4	16 Feb	Perceptron 1	CIML 4-4.5
	21 Feb	Perceptron 2 (proof of convergence)	CIML 4.5-4.7
5	23 Feb	Practical issues : debugging and evaluation	CIML 5-5.5
6	28 Feb	Practical issues: evaluation and imbalanced data	CIML 6-6.1
6	02 Mar	Beyond binary classification (imbalanced data & multiclass)	CIML -6.2
	07 Mar	MID TERM EXAM	CIML 1 -6, Lectures 1 - 11, All HW, Projects
	09 Mar	Collective Classification
	14 Mar	SNOW DAY!
	16 Mar	Linear Models	CIML 7-7.4
	21 Mar	Spring Break
	23 Mar	Spring Break
7	28 Mar	Linear models and gradient descent	CIML 7
		Lagrange Multipliers
	30 Mar	Probabilistic Models	CIML 9
8	04 Apr	Naive Bayes models	CIML 9
9	06 Apr	Linear SVM	CIML 7.5-7.7
	11 Apr	Neural networks I	CIML 10-10.3
10	13 Apr	Neural networks II	CIML 10.4-10.6
		Practice Problems
	18 Apr	Principal Components	CIML 15.2
	20 Aprl	Kernels	CIML 11-11.3
11	25 Apr	SVM II
	27 Apr	Deep learning - I	CIML - 10 plus web
12	2 May	Deep Learning - II	web
	4 May	Bias and Fairness	CIML 8
13	8 May	Wrap Up
	11 May	Practice	Practice Problems