Course Syllabus

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Study plan

Contact details

Student representatives

  • Camille F Porter
  • Daniel Adin
  • Lisa Bodlak

Course evaluation survey

Course evaluation survey: https://chalmers.instructure.com/courses/8183/files?preview=441960 

Course evaluation meeting protocol: https://chalmers.instructure.com/courses/8183/files?preview=441967

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Assignment information

  • The students may do the assignments in groups of one, two or three, depending on their preferences.
  • The Lab sessions are dedicated to the assignments.

Examination form

Assignments and the final take-home exam. The take-home exam will take place from Thursday 16 January 10:00 AM - Friday 17 January 12:00. 

Grading 

The final grade will be a combination of 60% of the final take-home exam and 40% of the lab assignments (i.e. each lab assignment will contribute 10% of the final grade). 

You need at least 50 points (50% of the maximum points) to pass. To get VG (Very Good) you need at least 80 points (80% of the maximum points).

 

Date and time Summary Topics Teacher Reading

Mon, Nov 4

13:15–15:00, ED

Lecture 1

PDF or ipynb-zip
  • Overview
  • Python data analysis & statistics
  • Random numbers
  • Basic simulations
 MP

Pandas
Seaborn
Matplotlib
random module in Numpy
Skiena, 2.2-2.3; 6.

Wed, Nov 6

13:15–17:00, ED3582

 Lab session

Assignment 1: Instructions

Due: November 13

 MP and TAs

 

Thu, Nov 7

10:00–11:45, FB

Lecture 2

PDF or ipynb-zip
  • Probabilistic models
  • Parameters
  • Naïve Bayes classification
 MP

Skiena: 11.1, optionally intro to 11 and 11.5.

Mitchell, , 1-2.

Mon, Nov 11

13:30 - 15:15, ED

Lecture 3

PDF or ipynb-zip
  • Discrete distributions
  • Some terminology
  • Modeling exercise
 MP

OpenIntro Statistics: 2.4, 3.3-3.5.

Distributions cheat sheet

Wed, Nov 13

13:15-17:00, ED3582

 Lab session

Assignment 2: Instructions

Due: November 26

 MP and TAs

Thu, Nov 14

10:00-11:45, EA

 

Lecture 4

PDF or ipynb-zip
  • Continuous distributions
  • Q-Q plots
  • Estimating parameters
  • Bayesian inference
 MP

MIT Open Courseware Prob/stats: 5b, 5c, 10b, 14a.

Bayesian intro

Mon, Nov 18

13:15-15:00, ED

Lecture 5

PDF or ipynb-zip
  • Interval estimates
  • Significance testing
  • Comparing two classifiers
  • p-value fishing
 MP

MIT OC Prob/stats: 17b, 22 [sec 5] , 24 .

Binomial test; McNemar test; Data dredging ; Hypotheses from data

Optional: Gelman & Loken

Wed, Nov 20

13:15-17:00, ED3582

Lab session

 Continue work on assignment 2 MP and TAs

Thu, Nov 21

10:0-11:45, HA2

Lecture 6

PDF

Linear regression models

 MHC

A First Course In Machine Learning, Chapter 1

Linear Regression with scikit-learn

Mon, Nov 25

13:15-15:00, ED

Lecture 7

to continue the previous lecture 

Linear regression, model selection

 MHC

Wed, Nov 27

13:15-17:00, ED3582

No lab session

 

 MHC and TAs

Thu, Nov 28

10:00-11:45, KA

Lecture 8

PDF

Probabilistic Regression

 MHC

A First Course In Machine Learning, 2.1, 2.2, 2.5.3, 2.5.4, 2.7, 2.8, 2.11

You may read 2.3, 2.4, 2.5, 2.6 for more information.

Mon, Dec 2

13:15-15:00, EC

Lecture 9

PDF

Bayesian Regression

 MHC A First Course In Machine Learning, 3.8

Wed, Dec 4

13:15-17:00

ED3582

 Lab session

Assignment 3, probabilistic regression

Due: December 13

 MHC and TAs

Thu, Dec 5

10:00-11:45, FB

 Lecture 10

to continue  Bayesian regression

 MHC

Mon, Dec 9

13:15-15:00, ED

Lecture 11

PDF

Probabilistic clustering

 MHC

Bishops' Book, Ch 9 (9.1 and 9.2)

GMM in Python 1

GMM in Python 2

Wed, Dec 11

13:15-15:00, ED3582

 Lab session

Assignment 4, GMM

Due: December 20

 MHC and TAs

Thu, Dec 12

10:00-11:45, FB

 Lecture 12

Probabilistic clustering

Review previous exam questions on clustering and linear regression

 MHC Sampling from a Normal Distribution

Mon, Dec 16

13:15-15:00, MA

Lecture 13

PDF or
  • Text clustering
  • Topic modeling
 MP

text processing in scikit-learn.

Blei.

Boyd-Graber videos:[introduction] [evaluation] [inference]

Wed, Dec 18

13:15-17:00, ED3582

 Lab session

Q&A session

 MP, MHC 

Thu, Dec 19

10:00-11:45, FB

Lecture 14

 Recap, preparing for the exam. MHC, MP

Take-home exam 2018 [solutions]

updated solution for GMM and Regression

Thu, 16 January 10:00 - Fri, 17 January 12:00

 Exam

 

Course Summary:

Date Details Due