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AT2.MDD 57 – Scientific programming with Julia
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00_KOMEETING.pdf
2019 - Lobianco - JuliaQuickSyntaxReference_ed1_online.pdf
◄ Program, Schedule and Grading
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Program, Schedule and Grading
SPJ - 00 KOM - 1: Course introduction (6:03)
SPJ - 00 KOM - 2: Julia overview (36:25)
SPJ - 00 KOM - 6A: Hands on #1 (20:15)
SPJ - 00 KOM - 7B: Hands on #2 (21:54)
SPJ - 00 KOM - 8: Pkg, modules and environments (20:56)
Quiz 0.1: Modules, packages and environments
SPJ - 00 KOM - 3: ML Terminology (21:19)
SPJ - 00 KOM - 4: A first ML Example (7:00)
SPJ - 00 KOM - 5: ML application areas (14:24)
SPJ - 00 KOM - 9: Further ML Examples (6:34)
SPJ - 01 JULIA1 - 1A: Basic syntax elements (Introduction and setup of the environment) (8:37)
SPJ - 01 JULIA1 - 1B: Basic syntax elements (Comments, code organisation, Unicode support, broadcasting) (12:04)
SPJ - 01 JULIA1 - 1C: Basic syntax elements (Math operators, quotation marks) (6:49)
Quiz 1.1: Basic syntax
SPJ - 01 JULIA1 - 1D: Basic syntax elements (Missing values) (10:04)
SPJ - 01 JULIA1 - 1E: Basic syntax elements (Stochasticity in programming) (9:14)
Quiz 1.2: Missingness and stochasticity
SPJ - 01 JULIA1 - 2A: Types and objects (Types, objects, variables and operators) (13:05)
SPJ - 01 JULIA1 - 2B: Types and objects (Object mutability and effects on copying objects) (13:34)
Quiz 1.3: Variables and objects
SPJ - 01 JULIA1 - 3A: Predefined types (Primitive types, char and strings) (9:37)
Quiz 1.4: Primitive types and strings
SPJ - 01 JULIA1 - 3B: Predefined types (One dimensional arrays) (30:42)
SPJ - 01 JULIA1 - 3C: Predefined types (Multidimensional arrays) (23:37)
Quiz 1.5: Arrays
SPJ - 01 JULIA1 - 3D: Predefined types (Tuples and named tuples) (7:50)
SPJ - 01 JULIA1 - 3E: Predefined types (Dictionaries and sets) (8:57)
SPJ - 01 JULIA1 - 3F: Predefined types (Date and times) (19:11)
Quiz 1.6: Other Data Structures
SPJ - 01 JULIA1 - 4A: Control flow and functions (Variables scope) (9:47)
SPJ - 01 JULIA1 - 4B: Control flow and functions (Loops and conditional statements) (9:2)
Quiz 1.7: Scope, loops and conditional statements
SPJ - 01 JULIA1 - 4C: Control flow and functions (Functions) (25:57)
Quiz 1.8: Functions
SPJ - 01 JULIA1 - 5A: Custom Types (Types of types, composite types) (17:23)
SPJ - 01 JULIA1 - 5B: Custom Types (Parametric types) (7:37)
Quiz 1.9: Custom types
SPJ - 01 JULIA1 - 5C: Custom Types (Inheritance and composition OO paradigms) (14:28)
Quiz 1.10: Inheritance and composition
SPJ - 01 JULIA1 - 6A: Further Topics (Metaprogramming and macros) (23:46)
Quiz 1.11: Metaprogramming and macros
SPJ - 01 JULIA1 - 6B: Further Topics (Interoperability with other languages) (23:6)
Quiz 1.12: Interoperability with other languages
SPJ - 01 JULIA1 - 6C: Further Topics (Performances and errors: profiling, debugging, introspection and exceptions) (27:33)
SPJ - 01 JULIA1 - 6D: Further Topics (Parallel computation: multithreading, multiprocessing) (20:3)
Quiz 1.13: Performances, parallel computation
Shelling Segregation Model Exercise
SPJ - 02 JULIA2 - 1A: Introduction and data import (18:28)
SPJ - 02 JULIA2 - 1B: Getting insights of the data (25:26)
SPJ - 02 JULIA2 - 1C: Edit data and dataframe structure (23:40)
SPJ - 02 JULIA2 - 1D: Pivot, Split-Apply-Combine and data export (23:39)
SPJ - 02 JULIA2 - 2A: Plotting (15:18)
SPJ - 02 JULIA2 - 2B: Probability distributions and data fitting (11:55)
The forest growth fitting problem
SPJ - 02 JULIA2 - 2C: Constrained optimisation, the transport problem (24:59)
SPJ - 02 JULIA2 - 2D: Nonlinear constrained optimisation, the optimal portfolio allocation (16:04)
The profit maximisation problem
SPMLJ - 03 ML1 - 1A: Introduction, perceptron overall idea (11:23)
SPMLJ - 03 ML1 - 1B: Hyperparameters and cross-validation (15:18)
SPMLJ - 03 ML1 - 1C: The perceptron algorithm (9:53)
SPMLJ - 03 ML1 - 1D: SVM and non-linear classification with linear classifiers (8:9)
SPMLJ - 03 ML1 - 2A: A first version (13:22)
SPMLJ - 03 ML1 - 2B: A better version (10:28)
SPMLJ - 03 ML1 - 2C: Cross-validation implementation (21:7)
Breast cancer diagnosis using the perceptron algorithm
SPMLJ - 04 NN - 1A: Introduction and motivations (5:32)
SPMLJ - 04 NN - 1B: Feed-forward neural networks (18:57)
SPMLJ - 04 NN - 1C: How to train a neural network (18:4)
SPMLJ - 04 NN - 1D: Convolutional neural networks (13:21)
SPMLJ - 04 NN - 1E: Multiple layers in convolutional neural networks (10:33)
SPMLJ - 04 NN - 1F: Recurrent neural networks (17:49)
SPMLJ - 04 NN - 2A: Binary classification (15:54)
SPMLJ - 04 NN - 2B: Multinomial classification (15:1)
SPMLJ - 04 NN - 2C: Regression (6:3)
SPMLJ - 04 NN - 2D: Convolutional neural networks (13:19)
House value prediction with Neural Networks (regression)
Wine class prediction with Neural Networks (multinomial classification)
SPJ - 00 KOM - 1: Course introduction (6:03) ►
