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Course: "Introduction to Artificial Intelligence"

Duration: 24 hours

The Learning Robots team has set up training courses at universities and engineering schools, in particular this 12-session, 2-hour course entitled "Introduction to Artificial Intelligence" given at Paris-Saclay University to first-year science students, most of whom are discovering programming in the Python language.
The course alternates between sessions on manipulating the robot in the software and sessions on programming Python algorithms that have been discovered previously, using the simulator instead.

The entire course is available in French at the following link (guest password: alphai).

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Here is the course outline:

➤ Session 1: Introduction to AI with the Autonomous Robot Race activity, accompanied by a presentation on the main concepts (AI model, training data, supervised learning, unsupervised learning, reinforcement learning, AI bias).

➤ Sessions 2 and 3: Learning Python programming with the robot (adaptation of the Camera Programming activity)

➤ Sessions 4 and 5: Introduction tothe K-nearest neighbors algorithm in the software (session 4), followed by its programming ( session 5)

➤ Session 6: Graded lab work

➤ Sessions 7 and 8: Discovering neural networks (including bias and intermediate neuron layers) in the software with the Intruder Detection activity (session 7), then using the scikit-learn library to create your first neural network (session 8)

➤ Sessions 9 and 10: Introduction to the Q-learning reinforcement learning algorithm in the software (session 9), followed by its programming (session 10)

➤ Session 11: Introductory course covering the functioning of generative AI and multimodal AI

➤ Session 12: Graded lab work

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It should be noted that this course follows the logical progression of our discovery sequence 4 Levels of Machine Autonomy! Students first learn to program the robot in the "traditional" way, then program Supervised Learning algorithms, and finally Reinforcement Learning algorithms.

This course provides material for many other courses at different levels. For example, we offer practical work in the Master's program in AI: the more advanced students are delighted to apply the algorithms they have learned to robotic equipment, which they are generally able to master quickly.

We also offer lighter versions of the course, lasting 8 or 12 hours, for example, limiting ourselves either to discovering the algorithms in the software without programming them, or limiting ourselves to programming the K nearest neighbor algorithm.

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