Organisation

Bachelor: 2h/week course and 2h/week laboratory, Winter semester
Lecturer: Sorin Grigorescu
Laboratory: Cosmin Ginerica, Liviu Marina
Language: Romanian

Courses

Lab	Description	Materials
1	Linear Algebra	details
2	Linear Regression	details
3	Logistic Regression	details
4	Naive Bayes	details
5	Neural Networks: Representation	details
6	Neural Networks: Gradients	details
7	Neural Networks: BackPropagation	details
8	Convolutional Neural Networks	details
9	Clustering	details
10	Principle Component Analysis	details

Laboratory

Lab	Description	Lab Materials	Code	Training Data	Test Data
1	Introduction	details	lab1.py	-	-
2	Linear Algebra	details	lab2.py	-	-
3	Linear Regression	details	linear_regression_ro.py	ex3x.txt, ex3y.txt	-
4	Logistic Regression	details	logistic_regression_ro.py, mapfeature.py, normalize_features.py	ex4x.txt, ex4y.txt	-
5	Naive Bayes	details	naive_bayes_ro.py	train-labels.txt,train-features-full.txt	test-labels.txt,test-features-full.txt
6	Neural Networks: Representation	details	lab6.py	-	-
7	Neural Networks: Gradients	details		-	-
8	Neural Networks: BackPropagation	details	lab8.py	nand_sum.txt	nand_sum_test.txt
9	Convolutional Neural Networks	details	tfmnist.py	train-images-idx3-ubyte.gz, train-labels-idx1-ubyte.gz	t10k-images-idx3-ubyte.gz, t10k-labels-idx1-ubyte.gz
10	Clustering	details	k_means_ro.py	lab10-data.txt	lab10-data-test.txt
11	Principle Component Analysis	details	pca_ro.py	-	-

Examination

Written and practical exam at the end of the semester.