Machine Learning Projects
Challenge: learn the foundations of machine learning
Actions:
concepts: linear regression, logistic regression, regularisation, neural networks, support vector machines, dimensionality reduction, principal component analysis, k-means clustering, anomaly detection, recommender systems, large scale machine learning
tools: Matlab / Octave
Challenge: train a convolution neural network to classify traffic signs images using the German Traffic Sign Data set; with the trained model classify traffic signs from the web
Actions:
explore, summarise and visualise the data set
design, train and test a model architecture
use the model to make predictions on new images
analyse the softmax probabilities of the new images
Challenge: train a convolution neural network to clone driving behaviour using training sets recorded in realistic video games
Actions:
use the simulator to collect data of good driving behaviour
build a convolution neural network in Keras that predicts steering angles from images
train and validate the model with a training and validation set
test that the model successfully drives around track without leaving the road
Challenge: write a software pipeline to identify the lane boundaries in a video taken while driving on a motorway
Actions:
compute the camera calibration matrix and distortion coefficients given a set of chessboard images.
apply a distortion correction on the video frames and save a corrected video
use colour transforms and gradients to create a threshold binary image
apply a perspective transform to rectify binary image ("birds-eye view")
detect lane pixels and fit to find the lane boundary
determine the curvature of the lane and vehicle position with respect to centre of curvature
warp the detected lane boundaries back onto the original perspective
output visual display of the lane boundaries and numerical estimation of lane curvature and vehicle position.
Challenge: write a software pipeline to detect vehicles in a video taken while driving on a motorway
Actions:
extract features from images using HOG (histogram of oriented gradients)
separate the images in train/test and train an SVM (support vector machine) classifier
implement a sliding window search and classify each window as vehicle or non-vehicle
output a video with the detected vehicles positions drawn as bounding boxes
Challenge: learn and practice the nuts and bolts of manipulating, processing, cleaning and crunching data in Python
Actions:
Learning Pandas Library by Matt Harrison [self paced study]
Python for Data Analysis by Wes McKinnney [self paced study]
tools: numpy, pandas, matplotlib, seaborn, Jupyter notebooks, scipy, scikit-learn
Challenge: learn and practice machine learning and computer vision
Actions:
Introduction to Machine Learning by Andreas Muller [self paced study]
Hands-on Machine Learning with scikit-learn and TensorFlow by Aurelien Geron [self paced study]
tools: scikit-learn, TensorFlow, keras, openCV, GPU
Challenge: use computing capabilities of Python to solve the nonlinear coupled partial derivative equations that govern the dynamics of fluids, the Navier-Stokes equations
Actions:
creating implicit numerical schemes to solve ever increasing difficult components of the NS equations: linear convection, nonlinear convection, diffusion, Burgers' equation, Laplace equation, Poisson equation
applying the full final code on two classical problems: cavity flow and channel flow
Last updated