# Google’s New Deep Learning MOOC Using TensorFlow

Deep learning became a hot topic in machine learning in the last 3-4 years (see inset below) and recently, Google released TensorFlow (a Python based deep learning toolkit) as an open source project to bring deep learning to everyone.￼

If you have wanted to get your hands dirty with TensorFlow or needed more direction with that, here’s some good news – Google is offering an open MOOC on deep learning methods using TensorFlow here. This course has been developed with Vincent Vanhoucke, Principal Scientist at Google, and technical lead in the Google Brain team. However, this is an intermediate to advanced level course and assumes you have taken a first course in machine learning, or that you are at least familiar with supervised learning methods.

Google’s overall goal in designing this course is to provide the machine learning enthusiast a rapid and direct path to solving real and interesting problems with deep learning techniques.

What is Deep Learning?

Course Overview

# Data Manipulation in R with dplyr – Part 3

This happens to be my 50th blog post – and my blog is 8 months old.

🙂

This post is the third and last post in in a series of posts (Part 1Part 2) on data manipulation with dlpyr. Note that the objects in the code may have been defined in earlier posts and the code in this post is in continuation with code from the earlier posts.

Although datasets can be manipulated in sophisticated ways by linking the 5 verbs of dplyr in conjunction, linking verbs together can be a bit verbose.

Creating multiple objects, especially when working on a large dataset can slow you down in your analysis. Chaining functions directly together into one line of code is difficult to read. This is sometimes called the Dagwood sandwich problem: you have too much filling (too many long arguments) between your slices of bread (parentheses). Functions and arguments get further and further apart.

The %>% operator allows you to extract the first argument of a function from the arguments list and put it in front of it, thus solving the Dagwood sandwich problem.

 # %>% OPERATOR ---------------------------------------------------------------------- # with %>% operator hflights %>% mutate(diff = TaxiOut - TaxiIn) %>% filter(!is.na(diff)) %>% summarise(avg = mean(diff)) # without %>% operator # arguments get further and further apart summarize(filter(mutate(hflights, diff = TaxiOut - TaxiIn),!is.na(diff)), avg = mean(diff)) # with %>% operator d <- hflights %>% select(Dest, UniqueCarrier, Distance, ActualElapsedTime) %>% mutate(RealTime = ActualElapsedTime + 100, mph = Distance/RealTime*60) # without %>% operator d <- mutate(select(hflights, Dest, UniqueCarrier, Distance, ActualElapsedTime), RealTime = ActualElapsedTime + 100, mph = Distance/RealTime*60) # Filter and summarise d d %>% filter(!is.na(mph), mph < 70) %>% summarise(n_less = n(), n_dest = n_distinct(Dest), min_dist = min(Distance), max_dist = max(Distance)) # Let's define preferable flights as flights that are 150% faster than driving, # i.e. that travel 105 mph or greater in real time. Also, assume that cancelled or # diverted flights are less preferable than driving. # ADVANCED PIPING EXERCISES # Use one single piped call to print a summary with the following variables: # n_non - the number of non-preferable flights in hflights, # p_non - the percentage of non-preferable flights in hflights, # n_dest - the number of destinations that non-preferable flights traveled to, # min_dist - the minimum distance that non-preferable flights traveled, # max_dist - the maximum distance that non-preferable flights traveled hflights %>% mutate(RealTime = ActualElapsedTime + 100, mph = Distance/RealTime*60) %>% filter(mph < 105 | Cancelled == 1 | Diverted == 1) %>% summarise(n_non = n(), p_non = 100*n_non/nrow(hflights), n_dest = n_distinct(Dest), min_dist = min(Distance), max_dist = max(Distance)) # Use summarise() to create a summary of hflights with a single variable, n, # that counts the number of overnight flights. These flights have an arrival # time that is earlier than their departure time. Only include flights that have # no NA values for both DepTime and ArrTime in your count. hflights %>% mutate(overnight = (ArrTime < DepTime)) %>% filter(overnight == TRUE) %>% summarise(n = n())
view raw pipingOperator.r hosted with ❤ by GitHub

group_by()

group_by() defines groups within a data set. Its influence becomes clear when calling summarise() on a grouped dataset. Summarizing statistics are calculated for the different groups separately.

 # group_by() ------------------------------------------------------------------------- # Generate a per-carrier summary of hflights with the following variables: n_flights, # the number of flights flown by the carrier; n_canc, the number of cancelled flights; # p_canc, the percentage of cancelled flights; avg_delay, the average arrival delay of # flights whose delay does not equal NA. Next, order the carriers in the summary from # low to high by their average arrival delay. Use percentage of flights cancelled to # break any ties. Which airline scores best based on these statistics? hflights %>% group_by(UniqueCarrier) %>% summarise(n_flights = n(), n_canc = sum(Cancelled), p_canc = 100*n_canc/n_flights, avg_delay = mean(ArrDelay, na.rm = TRUE)) %>% arrange(avg_delay) # Generate a per-day-of-week summary of hflights with the variable avg_taxi, # the average total taxiing time. Pipe this summary into an arrange() call such # that the day with the highest avg_taxi comes first. hflights %>% group_by(DayOfWeek) %>% summarize(avg_taxi = mean(TaxiIn + TaxiOut, na.rm = TRUE)) %>% arrange(desc(avg_taxi))
view raw group_by.R hosted with ❤ by GitHub

Combine group_by with mutate

group_by() can also be combined with mutate(). When you mutate grouped data, mutate() will calculate the new variables independently for each group. This is particularly useful when mutate() uses the rank() function, that calculates within group rankings. rank() takes a group of values and calculates the rank of each value within the group, e.g.

rank(c(21, 22, 24, 23))

has output

[1] 1 2 4 3

As with arrange(), rank() ranks values from the largest to the smallest and this behaviour can be reversed with the desc() function.

 # Combine group_by with mutate----- # First, discard flights whose arrival delay equals NA. Next, create a by-carrier # summary with a single variable: p_delay, the proportion of flights which are # delayed at arrival. Next, create a new variable rank in the summary which is a # rank according to p_delay. Finally, arrange the observations by this new rank hflights %>% filter(!is.na(ArrDelay)) %>% group_by(UniqueCarrier) %>% summarise(p_delay = sum(ArrDelay >0)/n()) %>% mutate(rank = rank(p_delay)) %>% arrange(rank) # n a similar fashion, keep flights that are delayed (ArrDelay > 0 and not NA). # Next, create a by-carrier summary with a single variable: avg, the average delay # of the delayed flights. Again add a new variable rank to the summary according to # avg. Finally, arrange by this rank variable. hflights %>% filter(!is.na(ArrDelay), ArrDelay > 0) %>% group_by(UniqueCarrier) %>% summarise(avg = mean(ArrDelay)) %>% mutate(rank = rank(avg)) %>% arrange(rank) # Advanced group_by exercises------------------------------------------------------- # Which plane (by tail number) flew out of Houston the most times? How many times? # Name the column with this frequency n. Assign the result to adv1. To answer this # question precisely, you will have to filter() as a final step to end up with only # a single observation in adv1. # Which plane (by tail number) flew out of Houston the most times? How many times? adv1 adv1 <- hflights %>% group_by(TailNum) %>% summarise(n = n()) %>% filter(n == max(n)) # How many airplanes only flew to one destination from Houston? adv2 # How many airplanes only flew to one destination from Houston? # Save the resulting dataset in adv2, that contains only a single column, # named nplanes and a single row. adv2 <- hflights %>% group_by(TailNum) %>% summarise(n_dest = n_distinct(Dest)) %>% filter(n_dest == 1) %>% summarise(nplanes = n()) # Find the most visited destination for each carrier and save your solution to adv3. # Your solution should contain four columns: # UniqueCarrier and Dest, # n, how often a carrier visited a particular destination, # rank, how each destination ranks per carrier. rank should be 1 for every row, # as you want to find the most visited destination for each carrier. adv3 <- hflights %>% group_by(UniqueCarrier, Dest) %>% summarise(n = n()) %>% mutate(rank = rank(desc(n))) %>% filter(rank == 1) # Find the carrier that travels to each destination the most: adv4 # For each destination, find the carrier that travels to that destination the most. # Store the result in adv4. Again, your solution should contain 4 columns: # Dest, UniqueCarrier, n and rank. adv4 <- hflights %>% group_by(Dest, UniqueCarrier) %>% summarise(n = n()) %>% mutate(rank = rank(desc(n))) %>% filter(rank == 1)

# My First Data Science Hackathon

So after 8 months of playing around with R and Python and blog post after blog post, I found myself finally hacking away at a problem set from the 17th storey of the Hindustan Times building at Connaught Place. I had entered my first ever data science hackathon conducted by Analytics Vidhya, a pioneer in analytics learning in India. Pizzas and Pepsi were on the house. Like any predictive analysis hackathon, this one accepted unlimited entries till submission time. It was from 2pm to 4:30pm today –  2.5 hours, of which I ended up wasting 1.5 hours trying to make my first submission which encountered submission error after submission error until the problem was fixed finally post lunch. I had 1 hour to try my best. It wasn’t the best performance, but I thought of blogging this experience anyway, as a reminder of the work that awaits me. I want to be the one winning prize money at the end of the day.

🙂

# Data Manipulation in R with dplyr – Part 2

Note that this post is in continuation with Part 1 of this series of posts on data manipulation with dplyr in R. The code in this post carries forward from the variables / objects defined in Part 1.

In the previous post, I talked about how dplyr provides a grammar of sorts to manipulate data, and consists of 5 verbs to do so:

The 5 verbs of dplyr
select – removes columns from a dataset
filter – removes rows from a dataset
arrange – reorders rows in a dataset
mutate – uses the data to build new columns and values
summarize – calculates summary statistics

I went on to discuss examples using select() and mutate(). Let’s now talk about filter(). R comes with a set of logical operators that you can use inside filter(). These operators are:
x < y, TRUE if x is less than y
x <= y, TRUE if x is less than or equal to y
x == y, TRUE if x equals y
x != y, TRUE if x does not equal y
x >= y, TRUE if x is greater than or equal to y
x > y, TRUE if x is greater than y
x %in% c(a, b, c), TRUE if x is in the vector c(a, b, c)

The following call, for example, filters df such that only the observations where the variable a is greater than the variable b:
filter(df, a > b)

 # Print out all flights in hflights that traveled 3000 or more miles filter(hflights, Distance > 3000) # All flights flown by one of JetBlue, Southwest, or Delta filter(hflights, UniqueCarrier %in% c('JetBlue', 'Southwest', 'Delta')) # All flights where taxiing took longer than flying filter(hflights, TaxiIn + TaxiOut > AirTime)
view raw verbs05.r hosted with ❤ by GitHub

Combining tests using boolean operators
R also comes with a set of boolean operators that you can use to combine multiple logical tests into a single test. These include & (and), | (or), and ! (not). Instead of using the & operator, you can also pass several logical tests to filter(), separated by commas. The following calls equivalent:

filter(df, a > b & c > d)
filter(df, a > b, c > d)

The is.na() will also come in handy very often. This expression, for example, keeps the observations in df for which the variable x is not NA:

filter(df, !is.na(x))

 # Combining tests using boolean operators # All flights that departed before 5am or arrived after 10pm filter(hflights, DepTime < 500 | ArrTime > 2200 ) # All flights that departed late but arrived ahead of schedule filter(hflights, DepDelay > 0 & ArrDelay < 0) # All cancelled weekend flights filter(hflights, DayOfWeek %in% c(6,7) & Cancelled == 1) # All flights that were cancelled after being delayed filter(hflights, Cancelled == 1, DepDelay > 0)
view raw verbs06.r hosted with ❤ by GitHub

A recap on select(), mutate() and filter():

 # Summarizing Exercise # Select the flights that had JFK as their destination: c1 c1 <- filter(hflights, Dest == 'JFK') # Combine the Year, Month and DayofMonth variables to create a Date column: c2 c2 <- mutate(c1, Date = paste(Year, Month, DayofMonth, sep = "-")) # Print out a selection of columns of c2 select(c2, Date, DepTime, ArrTime, TailNum) # How many weekend flights flew a distance of more than 1000 miles # but had a total taxiing time below 15 minutes? nrow(filter(hflights, DayOfWeek %in% c(6,7), Distance > 1000, TaxiIn + TaxiOut < 15))
view raw verbs07.r hosted with ❤ by GitHub

Arranging Data
arrange() can be used to rearrange rows according to any type of data. If you pass arrange() a character variable, R will rearrange the rows in alphabetical order according to values of the variable. If you pass a factor variable, R will rearrange the rows according to the order of the levels in your factor (running levels() on the variable reveals this order).

By default, arrange() arranges the rows from smallest to largest. Rows with the smallest value of the variable will appear at the top of the data set. You can reverse this behaviour with the desc() function. arrange() will reorder the rows from largest to smallest values of a variable if you wrap the variable name in desc() before passing it to arrange()

 # Definition of dtc dtc <- filter(hflights, Cancelled == 1, !is.na(DepDelay)) # Arrange dtc by departure delays arrange(dtc, DepDelay) # Arrange dtc so that cancellation reasons are grouped arrange(dtc, CancellationCode) # Arrange dtc according to carrier and departure delays arrange(dtc, UniqueCarrier, DepDelay) # Arrange according to carrier and decreasing departure delays arrange(hflights, UniqueCarrier, desc(DepDelay)) # Arrange flights by total delay (normal order). arrange(hflights, DepDelay + ArrDelay) # Keep flights leaving to DFW before 8am and arrange according to decreasing AirTime arrange(filter(hflights, Dest == 'DFW', DepTime < 800), desc(AirTime))
view raw verbs08.r hosted with ❤ by GitHub

Summarizing Data

summarise(), the last of the 5 verbs, follows the same syntax as mutate(), but the resulting dataset consists of a single row instead of an entire new column in the case of mutate().

In contrast to the four other data manipulation functions, summarise() does not return an altered copy of the dataset it is summarizing; instead, it builds a new dataset that contains only the summarizing statistics.

Note: summarise() and summarize() both work the same!

You can use any function you like in summarise(), so long as the function can take a vector of data and return a single number. R contains many aggregating functions. Here are some of the most useful:

min(x) – minimum value of vector x.
max(x) – maximum value of vector x.
mean(x) – mean value of vector x.
median(x) – median value of vector x.
quantile(x, p) – pth quantile of vector x.
sd(x) – standard deviation of vector x.
var(x) – variance of vector x.
IQR(x) – Inter Quartile Range (IQR) of vector x.
diff(range(x)) – total range of vector x.

 # Print out a summary with variables min_dist and max_dist summarize(hflights, min_dist = min(Distance), max_dist = max(Distance)) # Print out a summary with variable max_div summarize(filter(hflights, Diverted == 1), max_div = max(Distance)) # Remove rows that have NA ArrDelay: temp1 temp1 <- filter(hflights, !is.na(ArrDelay)) # Generate summary about ArrDelay column of temp1 summarise(temp1, earliest = min(ArrDelay), average = mean(ArrDelay), latest = max(ArrDelay), sd = sd(ArrDelay)) # Keep rows that have no NA TaxiIn and no NA TaxiOut: temp2 temp2 <- filter(hflights, !is.na(TaxiIn), !is.na(TaxiOut)) # Print the maximum taxiing difference of temp2 with summarise() summarise(temp2, max_taxi_diff = max(abs(TaxiIn - TaxiOut)))
view raw verbs09.r hosted with ❤ by GitHub

dplyr provides several helpful aggregate functions of its own, in addition to the ones that are already defined in R. These include:

first(x) – The first element of vector x.
last(x) – The last element of vector x.
nth(x, n) – The nth element of vector x.
n() – The number of rows in the data.frame or group of observations that summarise() describes.
n_distinct(x) – The number of unique values in vector x

 # Generate summarizing statistics for hflights summarise(hflights, n_obs = n(), n_carrier = n_distinct(UniqueCarrier), n_dest = n_distinct(Dest), dest100 = nth(Dest, 100)) # Filter hflights to keep all American Airline flights: aa aa <- filter(hflights, UniqueCarrier == "American") # Generate summarizing statistics for aa summarise(aa, n_flights = n(), n_canc = sum(Cancelled), p_canc = 100*(n_canc/n_flights), avg_delay = mean(ArrDelay, na.rm = TRUE))
view raw verbs10.r hosted with ❤ by GitHub

This would be it for Part-2 of this series of posts on data manipulation with dplyr. Part 3 would focus on the pipe operator, Group_by and working with databases.

# Data Manipulation in R with dplyr – Part 1

dplyr is one of the packages in R that makes R so loved by data scientists. It has three main goals:

1. Identify the most important data manipulation tools needed for data analysis and make them easy to use in R.
2. Provide blazing fast performance for in-memory data by writing key pieces of code in C++.
3. Use the same code interface to work with data no matter where it’s stored, whether in a data frame, a data table or database.

Introduction to the dplyr package and the tbl class
This post is mostly about code. If you’re interested in learning dplyr I recommend you type in the commands line by line on the R console to see first hand what’s happening.

 # INTRODUCTION TO dplyr AND tbls # Load the dplyr package library(dplyr) # Load the hflights package library(hflights) # Call both head() and summary() on hflights head(hflights) summary(hflights) # Convert the hflights data.frame into a hflights tbl hflights <- tbl_df(hflights) # Display the hflights tbl hflights # Create the object carriers, containing only the UniqueCarrier variable of hflights carriers <- hflights\$UniqueCarrier # Use lut to translate the UniqueCarrier column of hflights and before doing so # glimpse hflights to see the UniqueCarrier variablle glimpse(hflights) lut <- c("AA" = "American", "AS" = "Alaska", "B6" = "JetBlue", "CO" = "Continental", "DL" = "Delta", "OO" = "SkyWest", "UA" = "United", "US" = "US_Airways", "WN" = "Southwest", "EV" = "Atlantic_Southeast", "F9" = "Frontier", "FL" = "AirTran", "MQ" = "American_Eagle", "XE" = "ExpressJet", "YV" = "Mesa") hflights\$UniqueCarrier <- lut[hflights\$UniqueCarrier] # Now glimpse hflights to see the change in the UniqueCarrier variable glimpse(hflights) # Fill up empty entries of CancellationCode with 'E' # To do so, first index the empty entries in CancellationCode cancellationEmpty <- hflights\$CancellationCode == "" # Assign 'E' to the empty entries hflights\$CancellationCode[cancellationEmpty] <- 'E' # Use a new lookup table to create a vector of code labels. Assign the vector to the CancellationCode column of hflights lut = c('A' = 'carrier', 'B' = 'weather', 'C' = 'FFA', 'D' = 'security', 'E' = 'not cancelled') hflights\$CancellationCode <- lut[hflights\$CancellationCode] # Inspect the resulting raw values of your variables glimpse(hflights)
view raw introduction.R hosted with ❤ by GitHub

Select and mutate
dplyr provides grammar for data manipulation apart from providing data structure. The grammar is built around 5 functions (also referred to as verbs) that do the basic tasks of data manipulation.

The 5 verbs of dplyr
select – removes columns from a dataset
filter – removes rows from a dataset
arrange – reorders rows in a dataset
mutate – uses the data to build new columns and values
summarize – calculates summary statistics

dplyr functions do not change the dataset. They return a new copy of the dataset to use.

To answer the simple question whether flight delays tend to shrink or grow during a flight, we can safely discard a lot of the variables of each flight. To select only the ones that matter, we can use select()

 hflights[c('ActualElapsedTime','ArrDelay','DepDelay')] # Equivalently, using dplyr: select(hflights, ActualElapsedTime, ArrDelay, DepDelay) # Print out a tbl with the four columns of hflights related to delay select(hflights, ActualElapsedTime, AirTime, ArrDelay, DepDelay) # Print out hflights, nothing has changed! hflights # Print out the columns Origin up to Cancelled of hflights select(hflights, Origin:Cancelled) # Find the most concise way to select: columns Year up to and # including DayOfWeek, columns ArrDelay up to and including Diverted # Answer to last question: be concise! # You may want to examine the order of hflight's column names before you # begin with names() names(hflights) select(hflights, -(DepTime:AirTime))
view raw verbs01.R hosted with ❤ by GitHub

dplyr comes with a set of helper functions that can help you select variables. These functions find groups of variables to select, based on their names. Each of these works only when used inside of select()

• starts_with(“X”): every name that starts with “X”
• ends_with(“X”): every name that ends with “X”
• contains(“X”): every name that contains “X”
• matches(“X”): every name that matches “X”, where “X” can be a regular expression
• num_range(“x”, 1:5): the variables named x01, x02, x03, x04 and x05
• one_of(x): every name that appears in x, which should be a character vector
 # Helper functions used with dplyr # Print out a tbl containing just ArrDelay and DepDelay select(hflights, ArrDelay, DepDelay) # Use a combination of helper functions and variable names to print out # only the UniqueCarrier, FlightNum, TailNum, Cancelled, and CancellationCode # columns of hflights select(hflights, UniqueCarrier, FlightNum, contains("Tail"), contains("Cancel")) # Find the most concise way to return the following columns with select and its # helper functions: DepTime, ArrTime, ActualElapsedTime, AirTime, ArrDelay, # DepDelay. Use only helper functions select(hflights, ends_with("Time"), ends_with("Delay"))
view raw verbs02.R hosted with ❤ by GitHub

In order to appreciate the usefulness of dplyr, here are some comparisons between base R and dplyr

 # Some comparisons to basic R # both hflights and dplyr are available ex1r <- hflights[c("TaxiIn","TaxiOut","Distance")] ex1d <- select(hflights, TaxiIn, TaxiOut, Distance) ex2r <- hflights[c("Year","Month","DayOfWeek","DepTime","ArrTime")] ex2d <- select(hflights, Year:ArrTime, -DayofMonth) ex3r <- hflights[c("TailNum","TaxiIn","TaxiOut")] ex3d <- select(hflights, TailNum, contains("Taxi"))
view raw comparisons01.R hosted with ❤ by GitHub

mutate() is the second of the five data manipulation functions. mutate() creates new columns which are added to a copy of the dataset.

 # Add the new variable ActualGroundTime to a copy of hflights and save the result as g1. g1 <- mutate(hflights, ActualGroundTime = ActualElapsedTime - AirTime) # Add the new variable GroundTime to a g1. Save the result as g2. g2 <- mutate(g1, GroundTime = TaxiIn + TaxiOut) # Add the new variable AverageSpeed to g2. Save the result as g3. g3 <- mutate(g2, AverageSpeed = Distance / AirTime * 60) # Print out g3 g3
view raw verbs03.r hosted with ❤ by GitHub

So far we have added variables to hflights one at a time, but we can also use mutate() to add multiple variables at once.

 # Add a second variable loss_percent to the dataset: m1 m1 <- mutate(hflights, loss = ArrDelay - DepDelay, loss_percent = ((ArrDelay - DepDelay)/DepDelay)*100) # mutate() allows you to use a new variable while creating a next variable in the same call # Copy and adapt the previous command to reduce redendancy: m2 m2 <- mutate(hflights, loss = ArrDelay - DepDelay, loss_percent = (loss/DepDelay) * 100 ) # Add the three variables as described in the third instruction: m3 m3 <- mutate(hflights, TotalTaxi = TaxiIn + TaxiOut, ActualGroundTime = ActualElapsedTime - AirTime, Diff = TotalTaxi - ActualGroundTime)
view raw verbs04.r hosted with ❤ by GitHub

# Generating Permutation Matrices in Octave / Matlab

I have been doing Gilbert Strang’s linear algebra assignments, some of which require you to write short scripts in MatLab, though I use GNU Octave (which is kind of like a free MatLab). I was trying out this problem:

To solve this quickly, it would have been nice to have a function that would give a list of permutation matrices for every n-sized square matrix, but there was none in Octave, so I wrote a function permMatrices which creates a list of permutation matrices for a square matrix of size n.

 % function to generate permutation matrices given the size of the desired permutation matrices function x = permMatrices(n) x = zeros(n,n,factorial(n)); permutations = perms(1:n); for i = 1:size(x,3) x(:,:,i) = eye(n)(permutations(i,:),:); end endfunction
view raw permMatrices.m hosted with ❤ by GitHub

For example:

The MatLab / Octave code to solve this problem is shown below:

 % Solution for part (a) p = permMatrices(3); n = size(p,3); % number of permutation matrices v = zeros(n,1); % vector of zeros with dimension equalling number of permutation matrices % check for permutation matrices other than identity matrix with 3rd power equalling identity matrix for i = 1:n if p(:,:,i)^3 == eye(3) v(i,1) = 1; end end v(1,1) = 0; % exclude identity matrix ans1 = p(:,:,v == 1) % Solution for part (b) P = permMatrices(4); m = size(P,3); % number of permutation matrices t = zeros(m,1); % vector of zeros with dimension equalling number of permutation matrices % check for permutation matrices with 4th power equalling identity matrix for i = 1:m if P(:,:,i)^4 == eye(4) t(i,1) = 1; end end % print the permutation matrices ans2 = P(:,:,t == 0)
view raw Section2_7_13.m hosted with ❤ by GitHub

Output:

# Statistical Learning – 2016

On January 12, 2016, Stanford University professors Trevor Hastie and Rob Tibshirani will offer the 3rd iteration of Statistical Learning, a MOOC which first began in January 2014, and has become quite a popular course among data scientists. It is a great place to learn statistical learning (machine learning) methods using the R programming language. For a quick course on R, check this out – Introduction to R Programming

Slides and videos for Statistical Learning MOOC by Hastie and Tibshirani available separately here. Slides and video tutorials related to this book by Abass Al Sharif can be downloaded here.

The course covers the following book which is available for free as a PDF copy.

Logistics and Effort:

Rough Outline of Schedule (based on last year’s course offering):

Week 1: Introduction and Overview of Statistical Learning (Chapters 1-2)
Week 2: Linear Regression (Chapter 3)
Week 3: Classification (Chapter 4)
Week 4: Resampling Methods (Chapter 5)
Week 5: Linear Model Selection and Regularization (Chapter 6)
Week 6: Moving Beyond Linearity (Chapter 7)
Week 7: Tree-based Methods (Chapter 8)
Week 8: Support Vector Machines (Chapter 9)
Week 9: Unsupervised Learning (Chapter 10)

Prerequisites: First courses in statistics, linear algebra, and computing.

# Sherlock and the Beast – HackerRank

I found myself stuck on this problem recently. I must confess, I lost a couple of hours trying to get to figure the logic for this one. Here’s the problem:

I’ve written 2 functions to solve this problem. The first one I used for smaller N, say N < 30 and the second one for N > 30. The second function is elegant, and it relies on the mathematical property that if a number N is not divisible by 3, it could either leave a remainder 1 or 2.

If it leaves a remainder 2, then subtracting 5 once would make the number divisible by 3. If it leaves a remainder 1, then subtracting 5 twice would make the number divisible by 3.

We subtract 5 from N iteratively and attempt to divide N into 2 parts, one divisible by 3 and the other divisible by 5. We want the part that is divisible by 3 to be the larger part, so that the associated Decent Number is the largest possible. This explanation might seem obtuse, but if you get pen down on paper, you’ll understand what I mean.

Solution

# Supplementary Material to Andrew Ng’s Machine Learning MOOC

Although the lecture videos and lecture notes from Andrew Ng‘s Coursera MOOC are sufficient for the online version of the course, if you’re interested in more mathematical stuff or want to be challenged further, you can go through the following notes and problem sets from CS 229, a 10-week course that he teaches at Stanford (which also happens to be the most enrolled course on campus). It’s not hard to end up with a 100% score on his MOOC which is obviously a (much) watered down version of the course he teaches at Stanford, at least in terms of difficulty. If you don’t believe me, just have a go at the problem sets from the links below.

Lecture Notes

Section Notes

Handouts and Problem Sets

# Solutions to Machine Learning Programming Assignments

This post contains links to a bunch of code that I have written to complete Andrew Ng’s famous machine learning course which includes several interesting machine learning problems that needed to be solved using the Octave / Matlab programming language. I’m not sure I’d ever be programming in Octave after this course, but learning Octave just so that I could complete this course seemed worth the time and effort. I would usually work on the programming assignments on Sundays and spend several hours coding in Octave, telling myself that I would later replicate the exercises in Python.

If you’ve taken this course and found some of the assignments hard to complete, I think it might not hurt to go check online on how a particular function was implemented. If you end up copying the entire code, it’s probably your loss in the long run. But then John Maynard Keynes once said, ‘In the long run we are all dead‘. Yeah, and we wonder why people call Economics the dismal science!

Most people disregard Coursera’s feeble attempt at reigning in plagiarism by creating an Honor Code, precisely because this so-called code-of-conduct can be easily circumvented. I don’t mind posting solutions to a course’s programming assignments because GitHub is full to the brim with such content. Plus, it’s always good to read others’ code even if you implemented a function correctly. It helps understand the different ways of tackling a given programming problem.

Enjoy!