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Introduction to Machine Learning
Course Logistics
Varun ChandolaComputer Science & Engineering
State University of New York at BuffaloBuffalo, NY, USA
Chandola@UB CSE 474/574 1 / 18
Outline
Class Details
Syllabus
Textbooks
Grading
Gradiance
Python
Socrative Online
Honor Code
Checklist and Resources
Warmup
Chandola@UB CSE 474/574 2 / 18
Class Details
I Lecture InformationI Monday, Wednesday, Friday (9.00 - 9.50 AM)I 109 Knox
I Recitations
1. 10.00 - 10.50 AM Monday, Norton 2102. 01.00 - 01.50 PM Tuesday, Bell 3373. 08.00 - 08.50 AM Friday, Cooke 127a
I Recitation topics are listed in the syllabusI No recitation this week.
I Class web pageI http:
//www.cse.buffalo.edu/~chandola/machinelearning.htmlI https://piazza.com/buffalo/spring2018/cse474/home
Chandola@UB CSE 474/574 3 / 18
Instructor
I Varun ChandolaI http://www.cse.buffalo.edu/~chandolaI Email: [email protected] Office: 304 Davis HallI Phone: (716) 645-4747
I Office Hours: 1.00 PM - 3.00 PM (Mondays)
Chandola@UB CSE 474/574 4 / 18
Teaching Assistants
I Xin MaI Email: [email protected] Office Hours: 10.00 AM - 11.00 AM (Fridays)
I Rudra Prasad BakshiI Email: [email protected] Office Hours: 11.30 AM - 12.30 PM (Wednesdays)
I Hongfei XueI Email: [email protected] Office Hours: 4.00 PM - 5.00 PM (Mondays)
Chandola@UB CSE 474/574 5 / 18
Piazza
I Primary medium of communication
I All announcements, teaching notes, slides, polls, etc. will be madeavailable through Piazza.
I Questions?1. General post to all (Name will be visible).
I Choose appropriate folder.
2. Private post to instructor, TA.
I Interact.
Piazza IncentiveI Top 3 contributors (questions or answers) will get recognized
I Award - To be decided
Chandola@UB CSE 474/574 6 / 18
Topics Covered
Theoretical Machine Learning
I Concept Learning
I Mistake Bound Online Learning
I Vapnik-Chervonenkis Dimension
I PAC Learning
I Statistical Learning Theory
Machine Learning Tools
I Bayesian Inference
I Expectation Maximization
I Optimization
Machine Learning Algorithms
I Linear Regression
I Linear Classification
I Neural Networks
I Support Vector Machines
I Kernel Methods
I Latent Space Models (PCA)
I Mixture of Models
I Bayesian Networks
Chandola@UB CSE 474/574 7 / 18
Topics Covered
Theoretical Machine Learning
I Concept Learning
I Mistake Bound Online Learning
I Vapnik-Chervonenkis Dimension
I PAC Learning
I Statistical Learning Theory
Machine Learning Tools
I Bayesian Inference
I Expectation Maximization
I Optimization
Machine Learning Algorithms
I Linear Regression
I Linear Classification
I Neural Networks
I Support Vector Machines
I Kernel Methods
I Latent Space Models (PCA)
I Mixture of Models
I Bayesian Networks
Chandola@UB CSE 474/574 7 / 18
Topics Covered
Theoretical Machine Learning
I Concept Learning
I Mistake Bound Online Learning
I Vapnik-Chervonenkis Dimension
I PAC Learning
I Statistical Learning Theory
Machine Learning Tools
I Bayesian Inference
I Expectation Maximization
I Optimization
Machine Learning Algorithms
I Linear Regression
I Linear Classification
I Neural Networks
I Support Vector Machines
I Kernel Methods
I Latent Space Models (PCA)
I Mixture of Models
I Bayesian Networks
Chandola@UB CSE 474/574 7 / 18
Topics Not Covered
I Deep learning
I Reinforcement Learning
I Probabilistic Graphical Models and Bayesian Networks
I Decision Trees
I Association Analysis
I Applications of Machine Learning (See Piazza post)
Chandola@UB CSE 474/574 8 / 18
Textbooks
I No prescribed text
I Primary references
I Optional reading list
Chandola@UB CSE 474/574 9 / 18
Grading
I Grading SchemeI Short weekly quizzes using Gradiance (12) – 20%I Programming Assignments (3) – 30%I Mid-term Exam (in-class, open book/notes) on 03/16/2018 – 20%I Final Exam (in-class, open book/notes) on 05/16/2018 – 30%
I All components will be individually curved
I Final grade:
A [92.5, 100]A- [87.5, 92.5)B+ [82.5, 87.5)B [77.5, 82.5)
B- [72.5, 77.5)C+ [67.5, 72.5)C [62.5, 67.5)C- [57.5, 62.5)
I Use UBLearns for all electronic submissions
Chandola@UB CSE 474/574 10 / 18
Mid-term and Final Exams
I All material covered in classI Final exam will not be comprehensive
I All multi-choice objective problems
I No partial credit
Chandola@UB CSE 474/574 11 / 18
Gradiance
I An online quiz system
I One quiz per week released on Monday by 8.59 AM and due nextSunday by 11.59 PM
I 3 - 4 multiple choice problems about topics covered that week
I A warm up quiz (ungraded) is posted
I 5-minute delay between successive submissions
I Only 3 tries allowed, maximum score will be used
I Every wrong answer will result in 1 negative point per try
Gradiance EnrollmentI Go to http://www.newgradiance.com/services
I Register and use the class token FC4761F5
I Make sure you register using your UBIT name as the username
I No other username will be accepted
Chandola@UB CSE 474/574 12 / 18
Python
I All programming assigments and class demonstrations using Python
I Resources:I Installing python, ipythonI Python IDE - CanopyI More about ipython notebooksI Python for Developers, a complete book on Python programming by
Ricardo DuarteI CodeAmerica - PythonI An introduction to machine learning with Python and scikit-learn
(repo and overview) by Hannes Schulz and Andreas Mueller
Github RepoI https://github.com/ubdsgroup/ubmlcourse
I http://nbviewer.ipython.org/github/ubdsgroup/
ubmlcourse/tree/master/notebooks/
Chandola@UB CSE 474/574 13 / 18
Socrative Online
I Online student response systemI Random number generator!
I http://m.socrative.com/student/
I Enter class ID - 259432
I Optional
Chandola@UB CSE 474/574 14 / 18
Academic Integrity and Honor Code
I http://www.cse.buffalo.edu/shared/policies/academic.php
Chandola@UB CSE 474/574 15 / 18
Machine Learning Honor Code
I Against the ML honor code to:
1. Collaborate on Gradiance quizzes2. Collaborate or cheat during exams3. Submit someone else’s work, including from the internet, as one’s
own for any submission4. Misuse Piazza forum
I You are allowed to:
1. Have discussions about homeworks. Every student should submitown homework with names of students in the discussion groupexplicitly mentioned.
2. Collaborate in groups of 2 or 3 for programming assignments. Onesubmission is required for each group.
I Violation of ML honor code and departmental policy will result in anautomatic F for the concerned submission
I Two violations ⇒ fail grade in the course
Chandola@UB CSE 474/574 16 / 18
Checklist and Resources
1. Sign-up for Piazza
2. Sign-up for Gradiance, try warm-up quiz
3. Read the department’s academic integrity policy
ResourcesI Piazza - piazza.com/buffalo/spring2018/cse474/home
I Video Channel - TBA
I Course slides and handouts -www.cse.buffalo.edu/~chandola/machinelearning.html
I Github Repo - github.com/ubdsgroup/ubmlcourse
I Notebooks - nbviewer.ipython.org/github/ubdsgroup/ubmlcourse/tree/master/
Chandola@UB CSE 474/574 17 / 18
Warmup Exercise
A fair coinI Probability of heads?
I 5 heads in a row?
I 5th head after seen 4 heads in arow?
I Gambler’s Fallacy
I If I know that probability of twopeople bringing a bomb on aplane is very low, should I bringa bomb along to make myselfsafer?
Matrix Vector ProductsI Let [3, 4] denote a vector in a
2D space
I Multiply with a number?
2
[34
]I Multiply with a matrix?[
2 1−2 3
] [34
]I For a matrix, find a vector such
that matrix-vector product ≡scalar-vector product.
Chandola@UB CSE 474/574 18 / 18
Warmup Exercise
A fair coinI Probability of heads?
I 5 heads in a row?
I 5th head after seen 4 heads in arow?
I Gambler’s Fallacy
I If I know that probability of twopeople bringing a bomb on aplane is very low, should I bringa bomb along to make myselfsafer?
Matrix Vector ProductsI Let [3, 4] denote a vector in a
2D space
I Multiply with a number?
2
[34
]I Multiply with a matrix?[
2 1−2 3
] [34
]I For a matrix, find a vector such
that matrix-vector product ≡scalar-vector product.
Chandola@UB CSE 474/574 18 / 18
Warmup Exercise
A fair coinI Probability of heads?
I 5 heads in a row?
I 5th head after seen 4 heads in arow?
I Gambler’s Fallacy
I If I know that probability of twopeople bringing a bomb on aplane is very low, should I bringa bomb along to make myselfsafer?
Matrix Vector ProductsI Let [3, 4] denote a vector in a
2D space
I Multiply with a number?
2
[34
]I Multiply with a matrix?[
2 1−2 3
] [34
]I For a matrix, find a vector such
that matrix-vector product ≡scalar-vector product.
Chandola@UB CSE 474/574 18 / 18
Warmup Exercise
A fair coinI Probability of heads?
I 5 heads in a row?
I 5th head after seen 4 heads in arow?
I Gambler’s Fallacy
I If I know that probability of twopeople bringing a bomb on aplane is very low, should I bringa bomb along to make myselfsafer?
Matrix Vector ProductsI Let [3, 4] denote a vector in a
2D space
I Multiply with a number?
2
[34
]I Multiply with a matrix?[
2 1−2 3
] [34
]I For a matrix, find a vector such
that matrix-vector product ≡scalar-vector product.
Chandola@UB CSE 474/574 18 / 18